Semantic alignment of ontologies meaningful categories with the generalization of descriptive structures
The presented work addresses the issue of semantic alignment of ontology components with a generalized structured corpus. The field of research refers to the sphere of determining the features of trust in artificial intelligence. An alignment method is proposed at the level of semantic components of...
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pp_isofts_kiev_ua-article-5362023-06-25T07:49:58Z Semantic alignment of ontologies meaningful categories with the generalization of descriptive structures Метод семантичного визначення відповідності змістовних категорій онтологій із узагальненням описових структур Manziuk, E.A. Barmak, O.V. Krak, Iu.V. Pasichnyk, O.A. Radiuk, P.M. Mazurets, O.V. semantic alignment; ontology; information scope; entities UDC 004.896:004.912:004.048 семантичне вирівнювання; онтологія; інформаційна рамка; сутності УДК 004.896:004.912:004.048 The presented work addresses the issue of semantic alignment of ontology components with a generalized structured corpus. The field of research refers to the sphere of determining the features of trust in artificial intelligence. An alignment method is proposed at the level of semantic components of the general alignment system. The method is a component of a broader alignment system and compares entities at the level of meaningful correspondence. Moreover, only the alignment entities’ descriptive content is considered within the proposed technique. Descriptive contents can be represented by variously named id and semantic relations. The method defines a fundamental ontol- ogy and a specific alignment structure. Semantic correspondence in the form of information scope is formed from the alignment structure. In this way, an entity is formed on the side of the alignment structure, which would correspond in the best meaningful way to the entity from the ontology in terms of meaningful descriptiveness. Meaningful descriptiveness is the filling of information scope. Information scopes are formed as a final form of generalization and can consist of entities, a set of entities, and their partial union. In turn, entities are a generalization of properties that are located at a lower level of the hierarchy and, in turn, are a combination of descriptors. Descriptors are a fundamental element of generalization that represent principal content. Descriptors can define atomic content within a knowledge base and represent only a particular aspect of the content. Thus, the element of meaningfulness is not self-sufficient and can manifest as separate meaningfulness in the form of a property, as a minimal representation of the meaningfulness of an alignment. Descriptors can also supplement the content at the level of information frameworks, entities, and properties. The essence of the alignment in the form of information scope cannot be represented as a descriptor or their combination. It happens because the descriptive descriptor does not represent the content in the completed form of the correspondence unit. The minimum structure of representation of information scope is in the form of properties. This form of organization of establishing the correspondence of the semantic level of alignment allows you to structure and formalize the information content for areas with a complex form of semantic mapping. The hierarchical representation of the generalization not only allows simplifying the formalization of semantic alignment but also enables the formation of information entities with the possibility of discretization of content at the level of descriptors. In turn, descriptors can expand meaningfulness at an arbitrary level of the generalization hierarchy. This provides quantization of informational content and flexibility of the alignment system with discretization at the level of descriptors. The proposed method is used to formalize the semantic alignment of ontology entities and areas of structured representation of information.Prombles in programming 2022; 3-4: 355-363 В статті розглядається проблема семантичного порівняння складових онтології з узагальненим структурованим корпусом. Область дослідження відноситься сфери визначення складових довіри до штучного інтелекту. Пропонується метод порівняння на рівні семантичних складових загальної системи порівняння. Метод є складовою більш широкої системи порівняння та здійснює порівняння сутностей на рівні змістовної відповідності. В пропонованому методі враховуються тільки описові змістовності сутностей порівняння. Описові змістовності можуть бути представленні різними іменованими назвами та структурними зв’язками. В методі визначається базова онтологія та певна структура порівняння. Із структури порівняння формується семантична відпо- відність у вигляді інформаційних рамок. Таким чином на стороні структури порівняння формується сутність, яка б найкращим змістовним чином відповідала сутності з онтології за змістовною описовістю. Змістовна описовість є наповненням інформаційних рамок. Інформаційні рамки формується у вигляді кінцевої форми узагальнення та можуть складатися із сутностей, сукупнос- ті сутностей та їхнього часткового об’єднання. В свою чергу сутності є узагальненням властивостей, які розташовані на нижчому рівні ієрархії та свою чергу є поєднанням описових дескрипторів. Описові дескриптори є базовим елементом узагальнення та є представленням базової змістовності. Описові дескриптори можуть визначати атомарну змістовність в межах бази знань та представляють лише певний аспект змістовності. Таким аспект змістовності не є самодостатнім та можу проявлятись у виді відокремленої змістовності у вигляді властивості, як мінімальні формі представлення змістовності порівняння. Також описові дескрипторі можуть доповнювати змістовність на рівні інформаційних рамок, сутностей та властивостей. Сутність порівняння у вигляді інформаційних рамок не може бути представлена у вигляді описового дескриптора або їх поєднанням. Це зумовлено тим, що описових дескриптор не представляє змістовність в завершеній формі одиниці відповідності. Мінімальна форма представлення інформаційних рамок полягає у вигляді властивостей. Така форма організації встановлення відповідності семантичного рівня порівняння дозволяє структурувати та формалізувати інформаційну змістовність для областей із складною формою семантичного відображення. Ієрархічне представлення узагальнення дозволяє не тільки спростити формалізацію семантичного порівняння, а також дає змогу формувати інформаційні сутності із можливістю дискретизації змістовності на рівні описових дескрипторів. В свою чергу описові дескрипторі можуть розширити змістовність на довільному рівні ієрархії узагальнення. Це забезпечує квантування інформаційної змістовності та гнучкість системи порівняння з дискретизаціє на рівні описових дескрипторів. Запропонований метод використовується для формалізації семантичного порівняння сутностей онтології та областей структурованого представлення інформації. Prombles in programming 2022; 3-4: 355-363 Інститут програмних систем НАН України 2023-01-23 Article Article application/pdf https://pp.isofts.kiev.ua/index.php/ojs1/article/view/536 10.15407/pp2022.03-04.355 PROBLEMS IN PROGRAMMING; No 3-4 (2022); 355-363 ПРОБЛЕМЫ ПРОГРАММИРОВАНИЯ; No 3-4 (2022); 355-363 ПРОБЛЕМИ ПРОГРАМУВАННЯ; No 3-4 (2022); 355-363 1727-4907 10.15407/pp2022.03-04 en https://pp.isofts.kiev.ua/index.php/ojs1/article/view/536/589 Copyright (c) 2023 PROBLEMS IN PROGRAMMING |
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semantic alignment ontology information scope entities UDC 004.896:004.912:004.048 Manziuk, E.A. Barmak, O.V. Krak, Iu.V. Pasichnyk, O.A. Radiuk, P.M. Mazurets, O.V. Semantic alignment of ontologies meaningful categories with the generalization of descriptive structures |
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Manziuk, E.A. Barmak, O.V. Krak, Iu.V. Pasichnyk, O.A. Radiuk, P.M. Mazurets, O.V. |
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Semantic alignment of ontologies meaningful categories with the generalization of descriptive structures |
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Semantic alignment of ontologies meaningful categories with the generalization of descriptive structures |
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Semantic alignment of ontologies meaningful categories with the generalization of descriptive structures |
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Semantic alignment of ontologies meaningful categories with the generalization of descriptive structures |
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Semantic alignment of ontologies meaningful categories with the generalization of descriptive structures |
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semantic alignment of ontologies meaningful categories with the generalization of descriptive structures |
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Метод семантичного визначення відповідності змістовних категорій онтологій із узагальненням описових структур |
| description |
The presented work addresses the issue of semantic alignment of ontology components with a generalized structured corpus. The field of research refers to the sphere of determining the features of trust in artificial intelligence. An alignment method is proposed at the level of semantic components of the general alignment system. The method is a component of a broader alignment system and compares entities at the level of meaningful correspondence. Moreover, only the alignment entities’ descriptive content is considered within the proposed technique. Descriptive contents can be represented by variously named id and semantic relations. The method defines a fundamental ontol- ogy and a specific alignment structure. Semantic correspondence in the form of information scope is formed from the alignment structure. In this way, an entity is formed on the side of the alignment structure, which would correspond in the best meaningful way to the entity from the ontology in terms of meaningful descriptiveness. Meaningful descriptiveness is the filling of information scope. Information scopes are formed as a final form of generalization and can consist of entities, a set of entities, and their partial union. In turn, entities are a generalization of properties that are located at a lower level of the hierarchy and, in turn, are a combination of descriptors. Descriptors are a fundamental element of generalization that represent principal content. Descriptors can define atomic content within a knowledge base and represent only a particular aspect of the content. Thus, the element of meaningfulness is not self-sufficient and can manifest as separate meaningfulness in the form of a property, as a minimal representation of the meaningfulness of an alignment. Descriptors can also supplement the content at the level of information frameworks, entities, and properties. The essence of the alignment in the form of information scope cannot be represented as a descriptor or their combination. It happens because the descriptive descriptor does not represent the content in the completed form of the correspondence unit. The minimum structure of representation of information scope is in the form of properties. This form of organization of establishing the correspondence of the semantic level of alignment allows you to structure and formalize the information content for areas with a complex form of semantic mapping. The hierarchical representation of the generalization not only allows simplifying the formalization of semantic alignment but also enables the formation of information entities with the possibility of discretization of content at the level of descriptors. In turn, descriptors can expand meaningfulness at an arbitrary level of the generalization hierarchy. This provides quantization of informational content and flexibility of the alignment system with discretization at the level of descriptors. The proposed method is used to formalize the semantic alignment of ontology entities and areas of structured representation of information.Prombles in programming 2022; 3-4: 355-363 |
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Інститут програмних систем НАН України |
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355
Інформаційні системи
UDC 004.896:004.912:004.048 https://doi.org/10.15407/pp2022.03-04.355
SEMANTIC ALIGNMENT OF ONTOLOGIES
MEANINGFUL CATEGORIES WITH THE
GENERALIZATION OF DESCRIPTIVE STRUCTURES
Eduard Manziuk, Olexander Barmak, Iurii Krak,
Olexander Pasichnyk, Pavlo Radiuk, Olexander Mazurets
The presented work addresses the issue of semantic alignment of ontology components with a generalized structured corpus. The field of
research refers to the sphere of determining the features of trust in artificial intelligence. An alignment method is proposed at the level of
semantic components of the general alignment system. The method is a component of a broader alignment system and compares entities
at the level of meaningful correspondence. Moreover, only the alignment entities’ descriptive content is considered within the proposed
technique. Descriptive contents can be represented by variously named id and semantic relations. The method defines a fundamental ontol-
ogy and a specific alignment structure. Semantic correspondence in the form of information scope is formed from the alignment structure.
In this way, an entity is formed on the side of the alignment structure, which would correspond in the best meaningful way to the entity
from the ontology in terms of meaningful descriptiveness. Meaningful descriptiveness is the filling of information scope. Information
scopes are formed as a final form of generalization and can consist of entities, a set of entities, and their partial union. In turn, entities are
a generalization of properties that are located at a lower level of the hierarchy and, in turn, are a combination of descriptors. Descriptors
are a fundamental element of generalization that represent principal content. Descriptors can define atomic content within a knowledge
base and represent only a particular aspect of the content. Thus, the element of meaningfulness is not self-sufficient and can manifest as
separate meaningfulness in the form of a property, as a minimal representation of the meaningfulness of an alignment. Descriptors can
also supplement the content at the level of information frameworks, entities, and properties. The essence of the alignment in the form
of information scope cannot be represented as a descriptor or their combination. It happens because the descriptive descriptor does not
represent the content in the completed form of the correspondence unit. The minimum structure of representation of information scope
is in the form of properties. This form of organization of establishing the correspondence of the semantic level of alignment allows you
to structure and formalize the information content for areas with a complex form of semantic mapping. The hierarchical representation
of the generalization not only allows simplifying the formalization of semantic alignment but also enables the formation of information
entities with the possibility of discretization of content at the level of descriptors. In turn, descriptors can expand meaningfulness at an
arbitrary level of the generalization hierarchy. This provides quantization of informational content and flexibility of the alignment system
with discretization at the level of descriptors. The proposed method is used to formalize the semantic alignment of ontology entities and
areas of structured representation of information.
Keywords: semantic alignment, ontology, information scope, entities
В статті розглядається проблема семантичного порівняння складових онтології з узагальненим структурованим корпусом. Об-
ласть дослідження відноситься сфери визначення складових довіри до штучного інтелекту. Пропонується метод порівняння на
рівні семантичних складових загальної системи порівняння. Метод є складовою більш широкої системи порівняння та здійснює
порівняння сутностей на рівні змістовної відповідності. В пропонованому методі враховуються тільки описові змістовності сут-
ностей порівняння. Описові змістовності можуть бути представленні різними іменованими назвами та структурними зв’язками.
В методі визначається базова онтологія та певна структура порівняння. Із структури порівняння формується семантична відпо-
відність у вигляді інформаційних рамок. Таким чином на стороні структури порівняння формується сутність, яка б найкращим
змістовним чином відповідала сутності з онтології за змістовною описовістю. Змістовна описовість є наповненням інформацій-
них рамок. Інформаційні рамки формується у вигляді кінцевої форми узагальнення та можуть складатися із сутностей, сукупнос-
ті сутностей та їхнього часткового об’єднання. В свою чергу сутності є узагальненням властивостей, які розташовані на нижчому
рівні ієрархії та свою чергу є поєднанням описових дескрипторів. Описові дескриптори є базовим елементом узагальнення та
є представленням базової змістовності. Описові дескриптори можуть визначати атомарну змістовність в межах бази знань та
представляють лише певний аспект змістовності. Таким аспект змістовності не є самодостатнім та можу проявлятись у виді
відокремленої змістовності у вигляді властивості, як мінімальні формі представлення змістовності порівняння. Також описові
дескрипторі можуть доповнювати змістовність на рівні інформаційних рамок, сутностей та властивостей. Сутність порівняння у
вигляді інформаційних рамок не може бути представлена у вигляді описового дескриптора або їх поєднанням. Це зумовлено тим,
що описових дескриптор не представляє змістовність в завершеній формі одиниці відповідності. Мінімальна форма представлен-
ня інформаційних рамок полягає у вигляді властивостей. Така форма організації встановлення відповідності семантичного рівня
порівняння дозволяє структурувати та формалізувати інформаційну змістовність для областей із складною формою семантично-
го відображення. Ієрархічне представлення узагальнення дозволяє не тільки спростити формалізацію семантичного порівняння,
а також дає змогу формувати інформаційні сутності із можливістю дискретизації змістовності на рівні описових дескрипторів.
В свою чергу описові дескрипторі можуть розширити змістовність на довільному рівні ієрархії узагальнення. Це забезпечує
квантування інформаційної змістовності та гнучкість системи порівняння з дискретизаціє на рівні описових дескрипторів. Запро-
понований метод використовується для формалізації семантичного порівняння сутностей онтології та областей структурованого
представлення інформації.
Ключові слова: семантичне вирівнювання, онтологія, інформаційна рамка, сутності
Introduction
The need to establish the correspondence of concepts in different subject areas arises in connection with
the simultaneous and rapid development of applied research areas. Accordingly, a set of the diversity of formula-
tions and content representations is formed under such circumstances. The variety of such forms can also appear
for reasons unrelated to the specifics of the subject area. Since there is a wide field of similar research or those with
© У.Ф. Манзюк, О.В. Бармак, Ю.В. Крак, О.А. Пасічник, П.М. Радюк, О.В. Мазурець, 2022
ISSN 1727-4907. Проблеми програмування. 2022. № 3-4. Спеціальний випуск
356
Інформаційні системи
identical research essences in terms of meaningful comparison, there is a need to establish the correspondence of
the meaningful essences of the research subject areas. The need to develop the degree of entity alignment within
subject areas is topical both in cybersecurity [27, 28] and in other subject areas [7, 14, 20, 21, 26, 37], including in
machine learning methods [4–6, 19, 25]. Such an alignment is challenging due to the ambiguity of the interpretation
and the existing objective circumstance, which consists of incomplete correspondence. That is, there is a certain
inconsistency in the content of the entities. This discrepancy creates uncertainty that significantly affects the level of
compliance. In some circumstances, the existing non-conformity does not play a significant role; in other cases, the
same level of non-conformity can have a decisive effect. Determining the level of inconsistency is essential when
establishing the consistency of the entities of the alignment areas. However, there is a problem with formalization,
especially in the case of comparing the content of concepts that have the form of entities. In this regard, there is a
need to develop alignment methods that allow formalizing the establishment of correspondences relative to a par-
ticular basic set of entities.
The primary subject area of the alignment is determined, in which the essence of the alignment is defined.
The next stage is finding a representation of meaningful correspondence in the comparison field. Accordingly, there
is a need to define the basic unit of a certain amount of content. The following sections present the definition of cor-
respondences according to this descriptive content.
Related works
To determine the semantic alignment, let’s take several studies that are the closest in determining correspon-
dence and comparing entities from subject areas. They set the limits of informativeness in the form of a core, which
can be expanded by supplementing it with specific properties, using inversion, symmetry, intersection, union, and
other forms [11]. Although there are known structural alignment methods [9, 23], the semantic approach focuses on the
search for common meaningfulness. The semantic correspondence between gene ontology terms determined from gene
annotations and used within bioinformatics is determined [40]. The definition of similarity between entities is defined
based on finding a weighted path between concepts and finding distances between them [33]. It is essential to formalize
information from the subject area for further comparison [41], which is also manifested in forming comparison entities
for the field of electronic commerce when comparing goods [18]. The problem of establishing correspondence also
arises when comparing ontologies from the standpoint of multilingualism [15], and this necessity is also manifested
with the emergence of the Semantic Web [10]. The problem of knowledge alignment arises when comparing knowl-
edge bases that are represented in different languages [35].
At the same time, the alignment problem is relevant in comparing large ontologies, which requires the use of
automated methods for dividing ontologies into smaller areas of alignment [16] and comparing large knowledge bases
[34]. The alignment of large ontologies is carried out by automated clustering methods with subsequent alignment [30].
The computerized knowledge alignment systems are being developed but require human correction to improve quality
alignment [32]. However, it remains basic to determine the alignment quality based on experts’ assessments [2]. To
improve the automated alignment, machine learning methods are used [8, 24], including when using neural networks
[13] and random forest classifiers [31]. However, the quality of the automatic alignment largely depends on the quality
of the data [1]. Important is research towards ontology-based knowledge alignment, in which entities are aligned based
on their position in alignment ontologies [40].
Semantic knowledge alignment methods are most often used as a more straightforward form of knowledge
representation and alignment [37]. In this case, the alignment task is greatly facilitated since knowledge acquires a
more formalized representation. However, this requires pre-processing. Furthermore, the ambiguity problem when
comparing entities remains relevant when using knowledge bases [12, 42], which manifests itself when searching for
text similarity of content [36]. Another form of addition is the introduction of additional external information to fill the
significant gap between ontologies [3], which can be presented in the form of general-purpose background knowledge
[31]. The corpus is formed on many resources that independent parties developed with differences in the representation
of the same phenomenon in the real world. These resources are developed in pursuit of different goals and from a wide
range of applied areas, and the developers are geographically distant, affecting the seen and presented an image of the
real world in the research subject area.
According to the analysis, there is a significant part of the research on comparing ontologies entities that
concern particular areas and solve specific problems, for example, in the field of bioinformatics or construction. At
the same time, automatic methods have insufficient comparison quality and must be refined with a human’s help.
Yet, means and techniques that would help a person to standardize semantic comparison are insufficiently developed.
Therefore, there is an urgent need to establish a formalized approach to the discretization of the representation of in-
formativeness when establishing correspondences of the essences of ontologies.
Proposed semantic alignment
The basis of the developed method for the applied problem of comparison is an approach using knowledge
bases in the form of ontologies or schemes. This approach is also used for the comparison of ontologies, generalization,
etc. Taking this method as a basis, we will improve it for a specific case within the framework of the study of the corre-
spondence of the trust ontology to AI and the structured domain based on the gray literature corpus of the subject area.
The heterogeneity of knowledge-based data is a special case of the more general concept of diversity. Diversity, in gen-
eral, generates incompleteness of perception and causes the integration of knowledge that is presented in various forms
357
Інформаційні системи
of external representation in order to achieve the maximum possible perception that corresponds to the realities of the
surrounding world. Diversity is widespread in the description of the surrounding world. For the same phenomenon, ob-
servers will give different descriptions regarding meaningfulness, objectivity, necessity, and other factors. This is based
on different views, experiences, goals, language, etc., which is manifested in the complexity of knowledge integration.
When knowledge is formally represented in the form of a corpus of texts, semantic heterogeneity will
be presented in the form of the diversity of language
Інформаційні системи
[Введите текст]
observers will give different descriptions regarding meaningfulness, objectivity, necessity, and other factors. This is
based on different views, experiences, goals, language, etc., which is manifested in the complexity of knowledge
integration.
When knowledge is formally represented in the form of a corpus of texts, semantic heterogeneity will be
presented in the form of the diversity of language DivLang and diversity of knowledge DivKnow representation. Let’s
present the general diversity when comparing within the research as the diversity of informativeness
DivInf DivKnow DivLang= . Varieties of the language are manifested in linguistic phenomena (for example,
synonyms and homonyms for another).
The diversity of informativeness is manifested in the assumed absence of linguistic diversity in the diversity of
content of the entity and is key in semantic heterogeneity. The content of entities can be overdetermined and accumulate
the properties of less general entities, which is manifested by the absorption of other entities by the content, and partial
overlapping of the content of entities. To obtain a set of knowledge within a certain concept, which is presented in the
form of a named entity, let’s introduce a more general concept that summarizes entities at the meta-level and represents
a certain reference entity in relation to comparison tasks. Let’s denote the goal level of entities as ScIn (Scope
Information). The purpose of information frameworks is to generalize knowledge, which is presented in the form of a
set of heterogeneous entities and is actually a semantic representation of one and the same element of the surrounding
world. At the level of the concept, the presence of heterogeneity in the presentation of information in the sources is
assumed. Heterogeneity becomes the basis of the method and is not a problem since it practically manifests itself within
the boundaries of the corpus and, therefore, is known. Thus, the heterogeneity is limited by the corpus. The information
framework is initially unknown, although it is defined by knowledge and limited by the corpus.
We define the formal model of the information framework as follows
, ( ), .ScIn Ent Prop Des Scp= (1)
Here Ent - a set of named entities; ( )Prop Des - a set of properties that form named entities and are determined
by a set of descriptions; Scp - the range of properties is defined by a tuple.
( ) , ,Scp ent Scp ent ent Ent ent є сутністю ScIn= (2)
If defined, the framework of properties in relation to the entity
( ) Scp ent prop Prop prop є властивістю ent= (3)
The diversity of language DivLang is formalized in the presence of a set of named entities. Named entities can
formalize the same knowledge by a set of properties but have different names.
The diversity of knowledge DivKnow is formalized by quantizing knowledge in the form of properties
( )Prop Des . The same properties, for example, can form different named entities forming a non-empty intersection of
properties belonging to certain entities.
We will present the diversity of the language in the form of such concepts as synonyms and hyperonyms.
Linguistic correspondence at the level of synonyms
( , )
. . ( , ) ( , )
( , )synonym synonym ent ent
s t synonym ent en
m
n
atch en
y
t ent
t sy on m ent ent
=
(4)
To find synonyms, i.e., entities that have different nominal designations and a fully coincident set of property
descriptions defined ( ) ( ) , , ,ent ent ent O ent Ds ent entdsc dsc dsc Dsc dsc Dsc dsc dsc dsc .
Linguistic correspondence at the level of hyperonyms
( , )
. . ( , ) ( , )
( , )hyperonym hyperonym ent ent
s t hyperonym ent en
m
e
atch en
n
t ent
t hyp ro ym ent ent
=
(5)
and diversity of knowledge
Інформаційні системи
[Введите текст]
observers will give different descriptions regarding meaningfulness, objectivity, necessity, and other factors. This is
based on different views, experiences, goals, language, etc., which is manifested in the complexity of knowledge
integration.
When knowledge is formally represented in the form of a corpus of texts, semantic heterogeneity will be
presented in the form of the diversity of language DivLang and diversity of knowledge DivKnow representation. Let’s
present the general diversity when comparing within the research as the diversity of informativeness
DivInf DivKnow DivLang= . Varieties of the language are manifested in linguistic phenomena (for example,
synonyms and homonyms for another).
The diversity of informativeness is manifested in the assumed absence of linguistic diversity in the diversity of
content of the entity and is key in semantic heterogeneity. The content of entities can be overdetermined and accumulate
the properties of less general entities, which is manifested by the absorption of other entities by the content, and partial
overlapping of the content of entities. To obtain a set of knowledge within a certain concept, which is presented in the
form of a named entity, let’s introduce a more general concept that summarizes entities at the meta-level and represents
a certain reference entity in relation to comparison tasks. Let’s denote the goal level of entities as ScIn (Scope
Information). The purpose of information frameworks is to generalize knowledge, which is presented in the form of a
set of heterogeneous entities and is actually a semantic representation of one and the same element of the surrounding
world. At the level of the concept, the presence of heterogeneity in the presentation of information in the sources is
assumed. Heterogeneity becomes the basis of the method and is not a problem since it practically manifests itself within
the boundaries of the corpus and, therefore, is known. Thus, the heterogeneity is limited by the corpus. The information
framework is initially unknown, although it is defined by knowledge and limited by the corpus.
We define the formal model of the information framework as follows
, ( ), .ScIn Ent Prop Des Scp= (1)
Here Ent - a set of named entities; ( )Prop Des - a set of properties that form named entities and are determined
by a set of descriptions; Scp - the range of properties is defined by a tuple.
( ) , ,Scp ent Scp ent ent Ent ent є сутністю ScIn= (2)
If defined, the framework of properties in relation to the entity
( ) Scp ent prop Prop prop є властивістю ent= (3)
The diversity of language DivLang is formalized in the presence of a set of named entities. Named entities can
formalize the same knowledge by a set of properties but have different names.
The diversity of knowledge DivKnow is formalized by quantizing knowledge in the form of properties
( )Prop Des . The same properties, for example, can form different named entities forming a non-empty intersection of
properties belonging to certain entities.
We will present the diversity of the language in the form of such concepts as synonyms and hyperonyms.
Linguistic correspondence at the level of synonyms
( , )
. . ( , ) ( , )
( , )synonym synonym ent ent
s t synonym ent en
m
n
atch en
y
t ent
t sy on m ent ent
=
(4)
To find synonyms, i.e., entities that have different nominal designations and a fully coincident set of property
descriptions defined ( ) ( ) , , ,ent ent ent O ent Ds ent entdsc dsc dsc Dsc dsc Dsc dsc dsc dsc .
Linguistic correspondence at the level of hyperonyms
( , )
. . ( , ) ( , )
( , )hyperonym hyperonym ent ent
s t hyperonym ent en
m
e
atch en
n
t ent
t hyp ro ym ent ent
=
(5)
representa-
tion. Let’s present the general diversity when comparing within the research as the diversity of informativeness
Інформаційні системи
[Введите текст]
observers will give different descriptions regarding meaningfulness, objectivity, necessity, and other factors. This is
based on different views, experiences, goals, language, etc., which is manifested in the complexity of knowledge
integration.
When knowledge is formally represented in the form of a corpus of texts, semantic heterogeneity will be
presented in the form of the diversity of language DivLang and diversity of knowledge DivKnow representation. Let’s
present the general diversity when comparing within the research as the diversity of informativeness
DivInf DivKnow DivLang= . Varieties of the language are manifested in linguistic phenomena (for example,
synonyms and homonyms for another).
The diversity of informativeness is manifested in the assumed absence of linguistic diversity in the diversity of
content of the entity and is key in semantic heterogeneity. The content of entities can be overdetermined and accumulate
the properties of less general entities, which is manifested by the absorption of other entities by the content, and partial
overlapping of the content of entities. To obtain a set of knowledge within a certain concept, which is presented in the
form of a named entity, let’s introduce a more general concept that summarizes entities at the meta-level and represents
a certain reference entity in relation to comparison tasks. Let’s denote the goal level of entities as ScIn (Scope
Information). The purpose of information frameworks is to generalize knowledge, which is presented in the form of a
set of heterogeneous entities and is actually a semantic representation of one and the same element of the surrounding
world. At the level of the concept, the presence of heterogeneity in the presentation of information in the sources is
assumed. Heterogeneity becomes the basis of the method and is not a problem since it practically manifests itself within
the boundaries of the corpus and, therefore, is known. Thus, the heterogeneity is limited by the corpus. The information
framework is initially unknown, although it is defined by knowledge and limited by the corpus.
We define the formal model of the information framework as follows
, ( ), .ScIn Ent Prop Des Scp= (1)
Here Ent - a set of named entities; ( )Prop Des - a set of properties that form named entities and are determined
by a set of descriptions; Scp - the range of properties is defined by a tuple.
( ) , ,Scp ent Scp ent ent Ent ent є сутністю ScIn= (2)
If defined, the framework of properties in relation to the entity
( ) Scp ent prop Prop prop є властивістю ent= (3)
The diversity of language DivLang is formalized in the presence of a set of named entities. Named entities can
formalize the same knowledge by a set of properties but have different names.
The diversity of knowledge DivKnow is formalized by quantizing knowledge in the form of properties
( )Prop Des . The same properties, for example, can form different named entities forming a non-empty intersection of
properties belonging to certain entities.
We will present the diversity of the language in the form of such concepts as synonyms and hyperonyms.
Linguistic correspondence at the level of synonyms
( , )
. . ( , ) ( , )
( , )synonym synonym ent ent
s t synonym ent en
m
n
atch en
y
t ent
t sy on m ent ent
=
(4)
To find synonyms, i.e., entities that have different nominal designations and a fully coincident set of property
descriptions defined ( ) ( ) , , ,ent ent ent O ent Ds ent entdsc dsc dsc Dsc dsc Dsc dsc dsc dsc .
Linguistic correspondence at the level of hyperonyms
( , )
. . ( , ) ( , )
( , )hyperonym hyperonym ent ent
s t hyperonym ent en
m
e
atch en
n
t ent
t hyp ro ym ent ent
=
(5)
. Varieties of the language are manifested in linguistic phenomena (for example, syn-
onyms and homonyms for another).
The diversity of informativeness is manifested in the assumed absence of linguistic diversity in the
diversity of content of the entity and is key in semantic heterogeneity. The content of entities can be overde-
termined and accumulate the properties of less general entities, which is manifested by the absorption of other
entities by the content, and partial overlapping of the content of entities. To obtain a set of knowledge within
a certain concept, which is presented in the form of a named entity, let’s introduce a more general concept that
summarizes entities at the meta-level and represents a certain reference entity in relation to comparison tasks.
Let’s denote the goal level of entities as ScIn (Scope Information). The purpose of information frameworks
is to generalize knowledge, which is presented in the form of a set of heterogeneous entities and is actually a
semantic representation of one and the same element of the surrounding world. At the level of the concept, the
presence of heterogeneity in the presentation of information in the sources is assumed. Heterogeneity becomes
the basis of the method and is not a problem since it practically manifests itself within the boundaries of the
corpus and, therefore, is known. Thus, the heterogeneity is limited by the corpus. The information framework
is initially unknown, although it is defined by knowledge and limited by the corpus.
We define the formal model of the information framework as follows
Інформаційні системи
[Введите текст]
observers will give different descriptions regarding meaningfulness, objectivity, necessity, and other factors. This is
based on different views, experiences, goals, language, etc., which is manifested in the complexity of knowledge
integration.
When knowledge is formally represented in the form of a corpus of texts, semantic heterogeneity will be
presented in the form of the diversity of language DivLang and diversity of knowledge DivKnow representation. Let’s
present the general diversity when comparing within the research as the diversity of informativeness
DivInf DivKnow DivLang= . Varieties of the language are manifested in linguistic phenomena (for example,
synonyms and homonyms for another).
The diversity of informativeness is manifested in the assumed absence of linguistic diversity in the diversity of
content of the entity and is key in semantic heterogeneity. The content of entities can be overdetermined and accumulate
the properties of less general entities, which is manifested by the absorption of other entities by the content, and partial
overlapping of the content of entities. To obtain a set of knowledge within a certain concept, which is presented in the
form of a named entity, let’s introduce a more general concept that summarizes entities at the meta-level and represents
a certain reference entity in relation to comparison tasks. Let’s denote the goal level of entities as ScIn (Scope
Information). The purpose of information frameworks is to generalize knowledge, which is presented in the form of a
set of heterogeneous entities and is actually a semantic representation of one and the same element of the surrounding
world. At the level of the concept, the presence of heterogeneity in the presentation of information in the sources is
assumed. Heterogeneity becomes the basis of the method and is not a problem since it practically manifests itself within
the boundaries of the corpus and, therefore, is known. Thus, the heterogeneity is limited by the corpus. The information
framework is initially unknown, although it is defined by knowledge and limited by the corpus.
We define the formal model of the information framework as follows
, ( ), .ScIn Ent Prop Des Scp= (1)
Here Ent - a set of named entities; ( )Prop Des - a set of properties that form named entities and are determined
by a set of descriptions; Scp - the range of properties is defined by a tuple.
( ) , ,Scp ent Scp ent ent Ent ent є сутністю ScIn= (2)
If defined, the framework of properties in relation to the entity
( ) Scp ent prop Prop prop є властивістю ent= (3)
The diversity of language DivLang is formalized in the presence of a set of named entities. Named entities can
formalize the same knowledge by a set of properties but have different names.
The diversity of knowledge DivKnow is formalized by quantizing knowledge in the form of properties
( )Prop Des . The same properties, for example, can form different named entities forming a non-empty intersection of
properties belonging to certain entities.
We will present the diversity of the language in the form of such concepts as synonyms and hyperonyms.
Linguistic correspondence at the level of synonyms
( , )
. . ( , ) ( , )
( , )synonym synonym ent ent
s t synonym ent en
m
n
atch en
y
t ent
t sy on m ent ent
=
(4)
To find synonyms, i.e., entities that have different nominal designations and a fully coincident set of property
descriptions defined ( ) ( ) , , ,ent ent ent O ent Ds ent entdsc dsc dsc Dsc dsc Dsc dsc dsc dsc .
Linguistic correspondence at the level of hyperonyms
( , )
. . ( , ) ( , )
( , )hyperonym hyperonym ent ent
s t hyperonym ent en
m
e
atch en
n
t ent
t hyp ro ym ent ent
=
(5)
(1)
Here
Інформаційні системи
[Введите текст]
observers will give different descriptions regarding meaningfulness, objectivity, necessity, and other factors. This is
based on different views, experiences, goals, language, etc., which is manifested in the complexity of knowledge
integration.
When knowledge is formally represented in the form of a corpus of texts, semantic heterogeneity will be
presented in the form of the diversity of language DivLang and diversity of knowledge DivKnow representation. Let’s
present the general diversity when comparing within the research as the diversity of informativeness
DivInf DivKnow DivLang= . Varieties of the language are manifested in linguistic phenomena (for example,
synonyms and homonyms for another).
The diversity of informativeness is manifested in the assumed absence of linguistic diversity in the diversity of
content of the entity and is key in semantic heterogeneity. The content of entities can be overdetermined and accumulate
the properties of less general entities, which is manifested by the absorption of other entities by the content, and partial
overlapping of the content of entities. To obtain a set of knowledge within a certain concept, which is presented in the
form of a named entity, let’s introduce a more general concept that summarizes entities at the meta-level and represents
a certain reference entity in relation to comparison tasks. Let’s denote the goal level of entities as ScIn (Scope
Information). The purpose of information frameworks is to generalize knowledge, which is presented in the form of a
set of heterogeneous entities and is actually a semantic representation of one and the same element of the surrounding
world. At the level of the concept, the presence of heterogeneity in the presentation of information in the sources is
assumed. Heterogeneity becomes the basis of the method and is not a problem since it practically manifests itself within
the boundaries of the corpus and, therefore, is known. Thus, the heterogeneity is limited by the corpus. The information
framework is initially unknown, although it is defined by knowledge and limited by the corpus.
We define the formal model of the information framework as follows
, ( ), .ScIn Ent Prop Des Scp= (1)
Here Ent - a set of named entities; ( )Prop Des - a set of properties that form named entities and are determined
by a set of descriptions; Scp - the range of properties is defined by a tuple.
( ) , ,Scp ent Scp ent ent Ent ent є сутністю ScIn= (2)
If defined, the framework of properties in relation to the entity
( ) Scp ent prop Prop prop є властивістю ent= (3)
The diversity of language DivLang is formalized in the presence of a set of named entities. Named entities can
formalize the same knowledge by a set of properties but have different names.
The diversity of knowledge DivKnow is formalized by quantizing knowledge in the form of properties
( )Prop Des . The same properties, for example, can form different named entities forming a non-empty intersection of
properties belonging to certain entities.
We will present the diversity of the language in the form of such concepts as synonyms and hyperonyms.
Linguistic correspondence at the level of synonyms
( , )
. . ( , ) ( , )
( , )synonym synonym ent ent
s t synonym ent en
m
n
atch en
y
t ent
t sy on m ent ent
=
(4)
To find synonyms, i.e., entities that have different nominal designations and a fully coincident set of property
descriptions defined ( ) ( ) , , ,ent ent ent O ent Ds ent entdsc dsc dsc Dsc dsc Dsc dsc dsc dsc .
Linguistic correspondence at the level of hyperonyms
( , )
. . ( , ) ( , )
( , )hyperonym hyperonym ent ent
s t hyperonym ent en
m
e
atch en
n
t ent
t hyp ro ym ent ent
=
(5)
– a set of named entities;
Інформаційні системи
[Введите текст]
observers will give different descriptions regarding meaningfulness, objectivity, necessity, and other factors. This is
based on different views, experiences, goals, language, etc., which is manifested in the complexity of knowledge
integration.
When knowledge is formally represented in the form of a corpus of texts, semantic heterogeneity will be
presented in the form of the diversity of language DivLang and diversity of knowledge DivKnow representation. Let’s
present the general diversity when comparing within the research as the diversity of informativeness
DivInf DivKnow DivLang= . Varieties of the language are manifested in linguistic phenomena (for example,
synonyms and homonyms for another).
The diversity of informativeness is manifested in the assumed absence of linguistic diversity in the diversity of
content of the entity and is key in semantic heterogeneity. The content of entities can be overdetermined and accumulate
the properties of less general entities, which is manifested by the absorption of other entities by the content, and partial
overlapping of the content of entities. To obtain a set of knowledge within a certain concept, which is presented in the
form of a named entity, let’s introduce a more general concept that summarizes entities at the meta-level and represents
a certain reference entity in relation to comparison tasks. Let’s denote the goal level of entities as ScIn (Scope
Information). The purpose of information frameworks is to generalize knowledge, which is presented in the form of a
set of heterogeneous entities and is actually a semantic representation of one and the same element of the surrounding
world. At the level of the concept, the presence of heterogeneity in the presentation of information in the sources is
assumed. Heterogeneity becomes the basis of the method and is not a problem since it practically manifests itself within
the boundaries of the corpus and, therefore, is known. Thus, the heterogeneity is limited by the corpus. The information
framework is initially unknown, although it is defined by knowledge and limited by the corpus.
We define the formal model of the information framework as follows
, ( ), .ScIn Ent Prop Des Scp= (1)
Here Ent - a set of named entities; ( )Prop Des - a set of properties that form named entities and are determined
by a set of descriptions; Scp - the range of properties is defined by a tuple.
( ) , ,Scp ent Scp ent ent Ent ent є сутністю ScIn= (2)
If defined, the framework of properties in relation to the entity
( ) Scp ent prop Prop prop є властивістю ent= (3)
The diversity of language DivLang is formalized in the presence of a set of named entities. Named entities can
formalize the same knowledge by a set of properties but have different names.
The diversity of knowledge DivKnow is formalized by quantizing knowledge in the form of properties
( )Prop Des . The same properties, for example, can form different named entities forming a non-empty intersection of
properties belonging to certain entities.
We will present the diversity of the language in the form of such concepts as synonyms and hyperonyms.
Linguistic correspondence at the level of synonyms
( , )
. . ( , ) ( , )
( , )synonym synonym ent ent
s t synonym ent en
m
n
atch en
y
t ent
t sy on m ent ent
=
(4)
To find synonyms, i.e., entities that have different nominal designations and a fully coincident set of property
descriptions defined ( ) ( ) , , ,ent ent ent O ent Ds ent entdsc dsc dsc Dsc dsc Dsc dsc dsc dsc .
Linguistic correspondence at the level of hyperonyms
( , )
. . ( , ) ( , )
( , )hyperonym hyperonym ent ent
s t hyperonym ent en
m
e
atch en
n
t ent
t hyp ro ym ent ent
=
(5)
– a set of properties that form named entities and are determined
by a set of descriptions;
Інформаційні системи
[Введите текст]
observers will give different descriptions regarding meaningfulness, objectivity, necessity, and other factors. This is
based on different views, experiences, goals, language, etc., which is manifested in the complexity of knowledge
integration.
When knowledge is formally represented in the form of a corpus of texts, semantic heterogeneity will be
presented in the form of the diversity of language DivLang and diversity of knowledge DivKnow representation. Let’s
present the general diversity when comparing within the research as the diversity of informativeness
DivInf DivKnow DivLang= . Varieties of the language are manifested in linguistic phenomena (for example,
synonyms and homonyms for another).
The diversity of informativeness is manifested in the assumed absence of linguistic diversity in the diversity of
content of the entity and is key in semantic heterogeneity. The content of entities can be overdetermined and accumulate
the properties of less general entities, which is manifested by the absorption of other entities by the content, and partial
overlapping of the content of entities. To obtain a set of knowledge within a certain concept, which is presented in the
form of a named entity, let’s introduce a more general concept that summarizes entities at the meta-level and represents
a certain reference entity in relation to comparison tasks. Let’s denote the goal level of entities as ScIn (Scope
Information). The purpose of information frameworks is to generalize knowledge, which is presented in the form of a
set of heterogeneous entities and is actually a semantic representation of one and the same element of the surrounding
world. At the level of the concept, the presence of heterogeneity in the presentation of information in the sources is
assumed. Heterogeneity becomes the basis of the method and is not a problem since it practically manifests itself within
the boundaries of the corpus and, therefore, is known. Thus, the heterogeneity is limited by the corpus. The information
framework is initially unknown, although it is defined by knowledge and limited by the corpus.
We define the formal model of the information framework as follows
, ( ), .ScIn Ent Prop Des Scp= (1)
Here Ent - a set of named entities; ( )Prop Des - a set of properties that form named entities and are determined
by a set of descriptions; Scp - the range of properties is defined by a tuple.
( ) , ,Scp ent Scp ent ent Ent ent є сутністю ScIn= (2)
If defined, the framework of properties in relation to the entity
( ) Scp ent prop Prop prop є властивістю ent= (3)
The diversity of language DivLang is formalized in the presence of a set of named entities. Named entities can
formalize the same knowledge by a set of properties but have different names.
The diversity of knowledge DivKnow is formalized by quantizing knowledge in the form of properties
( )Prop Des . The same properties, for example, can form different named entities forming a non-empty intersection of
properties belonging to certain entities.
We will present the diversity of the language in the form of such concepts as synonyms and hyperonyms.
Linguistic correspondence at the level of synonyms
( , )
. . ( , ) ( , )
( , )synonym synonym ent ent
s t synonym ent en
m
n
atch en
y
t ent
t sy on m ent ent
=
(4)
To find synonyms, i.e., entities that have different nominal designations and a fully coincident set of property
descriptions defined ( ) ( ) , , ,ent ent ent O ent Ds ent entdsc dsc dsc Dsc dsc Dsc dsc dsc dsc .
Linguistic correspondence at the level of hyperonyms
( , )
. . ( , ) ( , )
( , )hyperonym hyperonym ent ent
s t hyperonym ent en
m
e
atch en
n
t ent
t hyp ro ym ent ent
=
(5)
– the range of properties is defined by a tuple.
Інформаційні системи
[Введите текст]
observers will give different descriptions regarding meaningfulness, objectivity, necessity, and other factors. This is
based on different views, experiences, goals, language, etc., which is manifested in the complexity of knowledge
integration.
When knowledge is formally represented in the form of a corpus of texts, semantic heterogeneity will be
presented in the form of the diversity of language DivLang and diversity of knowledge DivKnow representation. Let’s
present the general diversity when comparing within the research as the diversity of informativeness
DivInf DivKnow DivLang= . Varieties of the language are manifested in linguistic phenomena (for example,
synonyms and homonyms for another).
The diversity of informativeness is manifested in the assumed absence of linguistic diversity in the diversity of
content of the entity and is key in semantic heterogeneity. The content of entities can be overdetermined and accumulate
the properties of less general entities, which is manifested by the absorption of other entities by the content, and partial
overlapping of the content of entities. To obtain a set of knowledge within a certain concept, which is presented in the
form of a named entity, let’s introduce a more general concept that summarizes entities at the meta-level and represents
a certain reference entity in relation to comparison tasks. Let’s denote the goal level of entities as ScIn (Scope
Information). The purpose of information frameworks is to generalize knowledge, which is presented in the form of a
set of heterogeneous entities and is actually a semantic representation of one and the same element of the surrounding
world. At the level of the concept, the presence of heterogeneity in the presentation of information in the sources is
assumed. Heterogeneity becomes the basis of the method and is not a problem since it practically manifests itself within
the boundaries of the corpus and, therefore, is known. Thus, the heterogeneity is limited by the corpus. The information
framework is initially unknown, although it is defined by knowledge and limited by the corpus.
We define the formal model of the information framework as follows
, ( ), .ScIn Ent Prop Des Scp= (1)
Here Ent - a set of named entities; ( )Prop Des - a set of properties that form named entities and are determined
by a set of descriptions; Scp - the range of properties is defined by a tuple.
( ) , ,Scp ent Scp ent ent Ent ent є сутністю ScIn= (2)
If defined, the framework of properties in relation to the entity
( ) Scp ent prop Prop prop є властивістю ent= (3)
The diversity of language DivLang is formalized in the presence of a set of named entities. Named entities can
formalize the same knowledge by a set of properties but have different names.
The diversity of knowledge DivKnow is formalized by quantizing knowledge in the form of properties
( )Prop Des . The same properties, for example, can form different named entities forming a non-empty intersection of
properties belonging to certain entities.
We will present the diversity of the language in the form of such concepts as synonyms and hyperonyms.
Linguistic correspondence at the level of synonyms
( , )
. . ( , ) ( , )
( , )synonym synonym ent ent
s t synonym ent en
m
n
atch en
y
t ent
t sy on m ent ent
=
(4)
To find synonyms, i.e., entities that have different nominal designations and a fully coincident set of property
descriptions defined ( ) ( ) , , ,ent ent ent O ent Ds ent entdsc dsc dsc Dsc dsc Dsc dsc dsc dsc .
Linguistic correspondence at the level of hyperonyms
( , )
. . ( , ) ( , )
( , )hyperonym hyperonym ent ent
s t hyperonym ent en
m
e
atch en
n
t ent
t hyp ro ym ent ent
=
(5)
(2)
If defined, the framework of properties in relation to the entity
Інформаційні системи
[Введите текст]
observers will give different descriptions regarding meaningfulness, objectivity, necessity, and other factors. This is
based on different views, experiences, goals, language, etc., which is manifested in the complexity of knowledge
integration.
When knowledge is formally represented in the form of a corpus of texts, semantic heterogeneity will be
presented in the form of the diversity of language DivLang and diversity of knowledge DivKnow representation. Let’s
present the general diversity when comparing within the research as the diversity of informativeness
DivInf DivKnow DivLang= . Varieties of the language are manifested in linguistic phenomena (for example,
synonyms and homonyms for another).
The diversity of informativeness is manifested in the assumed absence of linguistic diversity in the diversity of
content of the entity and is key in semantic heterogeneity. The content of entities can be overdetermined and accumulate
the properties of less general entities, which is manifested by the absorption of other entities by the content, and partial
overlapping of the content of entities. To obtain a set of knowledge within a certain concept, which is presented in the
form of a named entity, let’s introduce a more general concept that summarizes entities at the meta-level and represents
a certain reference entity in relation to comparison tasks. Let’s denote the goal level of entities as ScIn (Scope
Information). The purpose of information frameworks is to generalize knowledge, which is presented in the form of a
set of heterogeneous entities and is actually a semantic representation of one and the same element of the surrounding
world. At the level of the concept, the presence of heterogeneity in the presentation of information in the sources is
assumed. Heterogeneity becomes the basis of the method and is not a problem since it practically manifests itself within
the boundaries of the corpus and, therefore, is known. Thus, the heterogeneity is limited by the corpus. The information
framework is initially unknown, although it is defined by knowledge and limited by the corpus.
We define the formal model of the information framework as follows
, ( ), .ScIn Ent Prop Des Scp= (1)
Here Ent - a set of named entities; ( )Prop Des - a set of properties that form named entities and are determined
by a set of descriptions; Scp - the range of properties is defined by a tuple.
( ) , ,Scp ent Scp ent ent Ent ent є сутністю ScIn= (2)
If defined, the framework of properties in relation to the entity
( ) Scp ent prop Prop prop є властивістю ent= (3)
The diversity of language DivLang is formalized in the presence of a set of named entities. Named entities can
formalize the same knowledge by a set of properties but have different names.
The diversity of knowledge DivKnow is formalized by quantizing knowledge in the form of properties
( )Prop Des . The same properties, for example, can form different named entities forming a non-empty intersection of
properties belonging to certain entities.
We will present the diversity of the language in the form of such concepts as synonyms and hyperonyms.
Linguistic correspondence at the level of synonyms
( , )
. . ( , ) ( , )
( , )synonym synonym ent ent
s t synonym ent en
m
n
atch en
y
t ent
t sy on m ent ent
=
(4)
To find synonyms, i.e., entities that have different nominal designations and a fully coincident set of property
descriptions defined ( ) ( ) , , ,ent ent ent O ent Ds ent entdsc dsc dsc Dsc dsc Dsc dsc dsc dsc .
Linguistic correspondence at the level of hyperonyms
( , )
. . ( , ) ( , )
( , )hyperonym hyperonym ent ent
s t hyperonym ent en
m
e
atch en
n
t ent
t hyp ro ym ent ent
=
(5)
(3)
The diversity of language
Інформаційні системи
[Введите текст]
observers will give different descriptions regarding meaningfulness, objectivity, necessity, and other factors. This is
based on different views, experiences, goals, language, etc., which is manifested in the complexity of knowledge
integration.
When knowledge is formally represented in the form of a corpus of texts, semantic heterogeneity will be
presented in the form of the diversity of language DivLang and diversity of knowledge DivKnow representation. Let’s
present the general diversity when comparing within the research as the diversity of informativeness
DivInf DivKnow DivLang= . Varieties of the language are manifested in linguistic phenomena (for example,
synonyms and homonyms for another).
The diversity of informativeness is manifested in the assumed absence of linguistic diversity in the diversity of
content of the entity and is key in semantic heterogeneity. The content of entities can be overdetermined and accumulate
the properties of less general entities, which is manifested by the absorption of other entities by the content, and partial
overlapping of the content of entities. To obtain a set of knowledge within a certain concept, which is presented in the
form of a named entity, let’s introduce a more general concept that summarizes entities at the meta-level and represents
a certain reference entity in relation to comparison tasks. Let’s denote the goal level of entities as ScIn (Scope
Information). The purpose of information frameworks is to generalize knowledge, which is presented in the form of a
set of heterogeneous entities and is actually a semantic representation of one and the same element of the surrounding
world. At the level of the concept, the presence of heterogeneity in the presentation of information in the sources is
assumed. Heterogeneity becomes the basis of the method and is not a problem since it practically manifests itself within
the boundaries of the corpus and, therefore, is known. Thus, the heterogeneity is limited by the corpus. The information
framework is initially unknown, although it is defined by knowledge and limited by the corpus.
We define the formal model of the information framework as follows
, ( ), .ScIn Ent Prop Des Scp= (1)
Here Ent - a set of named entities; ( )Prop Des - a set of properties that form named entities and are determined
by a set of descriptions; Scp - the range of properties is defined by a tuple.
( ) , ,Scp ent Scp ent ent Ent ent є сутністю ScIn= (2)
If defined, the framework of properties in relation to the entity
( ) Scp ent prop Prop prop є властивістю ent= (3)
The diversity of language DivLang is formalized in the presence of a set of named entities. Named entities can
formalize the same knowledge by a set of properties but have different names.
The diversity of knowledge DivKnow is formalized by quantizing knowledge in the form of properties
( )Prop Des . The same properties, for example, can form different named entities forming a non-empty intersection of
properties belonging to certain entities.
We will present the diversity of the language in the form of such concepts as synonyms and hyperonyms.
Linguistic correspondence at the level of synonyms
( , )
. . ( , ) ( , )
( , )synonym synonym ent ent
s t synonym ent en
m
n
atch en
y
t ent
t sy on m ent ent
=
(4)
To find synonyms, i.e., entities that have different nominal designations and a fully coincident set of property
descriptions defined ( ) ( ) , , ,ent ent ent O ent Ds ent entdsc dsc dsc Dsc dsc Dsc dsc dsc dsc .
Linguistic correspondence at the level of hyperonyms
( , )
. . ( , ) ( , )
( , )hyperonym hyperonym ent ent
s t hyperonym ent en
m
e
atch en
n
t ent
t hyp ro ym ent ent
=
(5)
is formalized in the presence of a set of named entities. Named entities can
formalize the same knowledge by a set of properties but have different names.
The diversity of knowledge
Інформаційні системи
[Введите текст]
observers will give different descriptions regarding meaningfulness, objectivity, necessity, and other factors. This is
based on different views, experiences, goals, language, etc., which is manifested in the complexity of knowledge
integration.
When knowledge is formally represented in the form of a corpus of texts, semantic heterogeneity will be
presented in the form of the diversity of language DivLang and diversity of knowledge DivKnow representation. Let’s
present the general diversity when comparing within the research as the diversity of informativeness
DivInf DivKnow DivLang= . Varieties of the language are manifested in linguistic phenomena (for example,
synonyms and homonyms for another).
The diversity of informativeness is manifested in the assumed absence of linguistic diversity in the diversity of
content of the entity and is key in semantic heterogeneity. The content of entities can be overdetermined and accumulate
the properties of less general entities, which is manifested by the absorption of other entities by the content, and partial
overlapping of the content of entities. To obtain a set of knowledge within a certain concept, which is presented in the
form of a named entity, let’s introduce a more general concept that summarizes entities at the meta-level and represents
a certain reference entity in relation to comparison tasks. Let’s denote the goal level of entities as ScIn (Scope
Information). The purpose of information frameworks is to generalize knowledge, which is presented in the form of a
set of heterogeneous entities and is actually a semantic representation of one and the same element of the surrounding
world. At the level of the concept, the presence of heterogeneity in the presentation of information in the sources is
assumed. Heterogeneity becomes the basis of the method and is not a problem since it practically manifests itself within
the boundaries of the corpus and, therefore, is known. Thus, the heterogeneity is limited by the corpus. The information
framework is initially unknown, although it is defined by knowledge and limited by the corpus.
We define the formal model of the information framework as follows
, ( ), .ScIn Ent Prop Des Scp= (1)
Here Ent - a set of named entities; ( )Prop Des - a set of properties that form named entities and are determined
by a set of descriptions; Scp - the range of properties is defined by a tuple.
( ) , ,Scp ent Scp ent ent Ent ent є сутністю ScIn= (2)
If defined, the framework of properties in relation to the entity
( ) Scp ent prop Prop prop є властивістю ent= (3)
The diversity of language DivLang is formalized in the presence of a set of named entities. Named entities can
formalize the same knowledge by a set of properties but have different names.
The diversity of knowledge DivKnow is formalized by quantizing knowledge in the form of properties
( )Prop Des . The same properties, for example, can form different named entities forming a non-empty intersection of
properties belonging to certain entities.
We will present the diversity of the language in the form of such concepts as synonyms and hyperonyms.
Linguistic correspondence at the level of synonyms
( , )
. . ( , ) ( , )
( , )synonym synonym ent ent
s t synonym ent en
m
n
atch en
y
t ent
t sy on m ent ent
=
(4)
To find synonyms, i.e., entities that have different nominal designations and a fully coincident set of property
descriptions defined ( ) ( ) , , ,ent ent ent O ent Ds ent entdsc dsc dsc Dsc dsc Dsc dsc dsc dsc .
Linguistic correspondence at the level of hyperonyms
( , )
. . ( , ) ( , )
( , )hyperonym hyperonym ent ent
s t hyperonym ent en
m
e
atch en
n
t ent
t hyp ro ym ent ent
=
(5)
is formalized by quantizing knowledge in the form of properties
Інформаційні системи
[Введите текст]
observers will give different descriptions regarding meaningfulness, objectivity, necessity, and other factors. This is
based on different views, experiences, goals, language, etc., which is manifested in the complexity of knowledge
integration.
When knowledge is formally represented in the form of a corpus of texts, semantic heterogeneity will be
presented in the form of the diversity of language DivLang and diversity of knowledge DivKnow representation. Let’s
present the general diversity when comparing within the research as the diversity of informativeness
DivInf DivKnow DivLang= . Varieties of the language are manifested in linguistic phenomena (for example,
synonyms and homonyms for another).
The diversity of informativeness is manifested in the assumed absence of linguistic diversity in the diversity of
content of the entity and is key in semantic heterogeneity. The content of entities can be overdetermined and accumulate
the properties of less general entities, which is manifested by the absorption of other entities by the content, and partial
overlapping of the content of entities. To obtain a set of knowledge within a certain concept, which is presented in the
form of a named entity, let’s introduce a more general concept that summarizes entities at the meta-level and represents
a certain reference entity in relation to comparison tasks. Let’s denote the goal level of entities as ScIn (Scope
Information). The purpose of information frameworks is to generalize knowledge, which is presented in the form of a
set of heterogeneous entities and is actually a semantic representation of one and the same element of the surrounding
world. At the level of the concept, the presence of heterogeneity in the presentation of information in the sources is
assumed. Heterogeneity becomes the basis of the method and is not a problem since it practically manifests itself within
the boundaries of the corpus and, therefore, is known. Thus, the heterogeneity is limited by the corpus. The information
framework is initially unknown, although it is defined by knowledge and limited by the corpus.
We define the formal model of the information framework as follows
, ( ), .ScIn Ent Prop Des Scp= (1)
Here Ent - a set of named entities; ( )Prop Des - a set of properties that form named entities and are determined
by a set of descriptions; Scp - the range of properties is defined by a tuple.
( ) , ,Scp ent Scp ent ent Ent ent є сутністю ScIn= (2)
If defined, the framework of properties in relation to the entity
( ) Scp ent prop Prop prop є властивістю ent= (3)
The diversity of language DivLang is formalized in the presence of a set of named entities. Named entities can
formalize the same knowledge by a set of properties but have different names.
The diversity of knowledge DivKnow is formalized by quantizing knowledge in the form of properties
( )Prop Des . The same properties, for example, can form different named entities forming a non-empty intersection of
properties belonging to certain entities.
We will present the diversity of the language in the form of such concepts as synonyms and hyperonyms.
Linguistic correspondence at the level of synonyms
( , )
. . ( , ) ( , )
( , )synonym synonym ent ent
s t synonym ent en
m
n
atch en
y
t ent
t sy on m ent ent
=
(4)
To find synonyms, i.e., entities that have different nominal designations and a fully coincident set of property
descriptions defined ( ) ( ) , , ,ent ent ent O ent Ds ent entdsc dsc dsc Dsc dsc Dsc dsc dsc dsc .
Linguistic correspondence at the level of hyperonyms
( , )
. . ( , ) ( , )
( , )hyperonym hyperonym ent ent
s t hyperonym ent en
m
e
atch en
n
t ent
t hyp ro ym ent ent
=
(5)
. The same properties, for example, can form different named entities forming a non-empty intersection of
properties belonging to certain entities.
We will present the diversity of the language in the form of such concepts as synonyms and hyperonyms.
Linguistic correspondence at the level of synonyms
Інформаційні системи
[Введите текст]
observers will give different descriptions regarding meaningfulness, objectivity, necessity, and other factors. This is
based on different views, experiences, goals, language, etc., which is manifested in the complexity of knowledge
integration.
When knowledge is formally represented in the form of a corpus of texts, semantic heterogeneity will be
presented in the form of the diversity of language DivLang and diversity of knowledge DivKnow representation. Let’s
present the general diversity when comparing within the research as the diversity of informativeness
DivInf DivKnow DivLang= . Varieties of the language are manifested in linguistic phenomena (for example,
synonyms and homonyms for another).
The diversity of informativeness is manifested in the assumed absence of linguistic diversity in the diversity of
content of the entity and is key in semantic heterogeneity. The content of entities can be overdetermined and accumulate
the properties of less general entities, which is manifested by the absorption of other entities by the content, and partial
overlapping of the content of entities. To obtain a set of knowledge within a certain concept, which is presented in the
form of a named entity, let’s introduce a more general concept that summarizes entities at the meta-level and represents
a certain reference entity in relation to comparison tasks. Let’s denote the goal level of entities as ScIn (Scope
Information). The purpose of information frameworks is to generalize knowledge, which is presented in the form of a
set of heterogeneous entities and is actually a semantic representation of one and the same element of the surrounding
world. At the level of the concept, the presence of heterogeneity in the presentation of information in the sources is
assumed. Heterogeneity becomes the basis of the method and is not a problem since it practically manifests itself within
the boundaries of the corpus and, therefore, is known. Thus, the heterogeneity is limited by the corpus. The information
framework is initially unknown, although it is defined by knowledge and limited by the corpus.
We define the formal model of the information framework as follows
, ( ), .ScIn Ent Prop Des Scp= (1)
Here Ent - a set of named entities; ( )Prop Des - a set of properties that form named entities and are determined
by a set of descriptions; Scp - the range of properties is defined by a tuple.
( ) , ,Scp ent Scp ent ent Ent ent є сутністю ScIn= (2)
If defined, the framework of properties in relation to the entity
( ) Scp ent prop Prop prop є властивістю ent= (3)
The diversity of language DivLang is formalized in the presence of a set of named entities. Named entities can
formalize the same knowledge by a set of properties but have different names.
The diversity of knowledge DivKnow is formalized by quantizing knowledge in the form of properties
( )Prop Des . The same properties, for example, can form different named entities forming a non-empty intersection of
properties belonging to certain entities.
We will present the diversity of the language in the form of such concepts as synonyms and hyperonyms.
Linguistic correspondence at the level of synonyms
( , )
. . ( , ) ( , )
( , )synonym synonym ent ent
s t synonym ent en
m
n
atch en
y
t ent
t sy on m ent ent
=
(4)
To find synonyms, i.e., entities that have different nominal designations and a fully coincident set of property
descriptions defined ( ) ( ) , , ,ent ent ent O ent Ds ent entdsc dsc dsc Dsc dsc Dsc dsc dsc dsc .
Linguistic correspondence at the level of hyperonyms
( , )
. . ( , ) ( , )
( , )hyperonym hyperonym ent ent
s t hyperonym ent en
m
e
atch en
n
t ent
t hyp ro ym ent ent
=
(5)
(4)
To find synonyms, i.e., entities that have different nominal designations and a fully coincident set of property
descriptions defined
Інформаційні системи
[Введите текст]
observers will give different descriptions regarding meaningfulness, objectivity, necessity, and other factors. This is
based on different views, experiences, goals, language, etc., which is manifested in the complexity of knowledge
integration.
When knowledge is formally represented in the form of a corpus of texts, semantic heterogeneity will be
presented in the form of the diversity of language DivLang and diversity of knowledge DivKnow representation. Let’s
present the general diversity when comparing within the research as the diversity of informativeness
DivInf DivKnow DivLang= . Varieties of the language are manifested in linguistic phenomena (for example,
synonyms and homonyms for another).
The diversity of informativeness is manifested in the assumed absence of linguistic diversity in the diversity of
content of the entity and is key in semantic heterogeneity. The content of entities can be overdetermined and accumulate
the properties of less general entities, which is manifested by the absorption of other entities by the content, and partial
overlapping of the content of entities. To obtain a set of knowledge within a certain concept, which is presented in the
form of a named entity, let’s introduce a more general concept that summarizes entities at the meta-level and represents
a certain reference entity in relation to comparison tasks. Let’s denote the goal level of entities as ScIn (Scope
Information). The purpose of information frameworks is to generalize knowledge, which is presented in the form of a
set of heterogeneous entities and is actually a semantic representation of one and the same element of the surrounding
world. At the level of the concept, the presence of heterogeneity in the presentation of information in the sources is
assumed. Heterogeneity becomes the basis of the method and is not a problem since it practically manifests itself within
the boundaries of the corpus and, therefore, is known. Thus, the heterogeneity is limited by the corpus. The information
framework is initially unknown, although it is defined by knowledge and limited by the corpus.
We define the formal model of the information framework as follows
, ( ), .ScIn Ent Prop Des Scp= (1)
Here Ent - a set of named entities; ( )Prop Des - a set of properties that form named entities and are determined
by a set of descriptions; Scp - the range of properties is defined by a tuple.
( ) , ,Scp ent Scp ent ent Ent ent є сутністю ScIn= (2)
If defined, the framework of properties in relation to the entity
( ) Scp ent prop Prop prop є властивістю ent= (3)
The diversity of language DivLang is formalized in the presence of a set of named entities. Named entities can
formalize the same knowledge by a set of properties but have different names.
The diversity of knowledge DivKnow is formalized by quantizing knowledge in the form of properties
( )Prop Des . The same properties, for example, can form different named entities forming a non-empty intersection of
properties belonging to certain entities.
We will present the diversity of the language in the form of such concepts as synonyms and hyperonyms.
Linguistic correspondence at the level of synonyms
( , )
. . ( , ) ( , )
( , )synonym synonym ent ent
s t synonym ent en
m
n
atch en
y
t ent
t sy on m ent ent
=
(4)
To find synonyms, i.e., entities that have different nominal designations and a fully coincident set of property
descriptions defined ( ) ( ) , , ,ent ent ent O ent Ds ent entdsc dsc dsc Dsc dsc Dsc dsc dsc dsc .
Linguistic correspondence at the level of hyperonyms
( , )
. . ( , ) ( , )
( , )hyperonym hyperonym ent ent
s t hyperonym ent en
m
e
atch en
n
t ent
t hyp ro ym ent ent
=
(5)
Linguistic correspondence at the level of hyperonyms
Інформаційні системи
[Введите текст]
observers will give different descriptions regarding meaningfulness, objectivity, necessity, and other factors. This is
based on different views, experiences, goals, language, etc., which is manifested in the complexity of knowledge
integration.
When knowledge is formally represented in the form of a corpus of texts, semantic heterogeneity will be
presented in the form of the diversity of language DivLang and diversity of knowledge DivKnow representation. Let’s
present the general diversity when comparing within the research as the diversity of informativeness
DivInf DivKnow DivLang= . Varieties of the language are manifested in linguistic phenomena (for example,
synonyms and homonyms for another).
The diversity of informativeness is manifested in the assumed absence of linguistic diversity in the diversity of
content of the entity and is key in semantic heterogeneity. The content of entities can be overdetermined and accumulate
the properties of less general entities, which is manifested by the absorption of other entities by the content, and partial
overlapping of the content of entities. To obtain a set of knowledge within a certain concept, which is presented in the
form of a named entity, let’s introduce a more general concept that summarizes entities at the meta-level and represents
a certain reference entity in relation to comparison tasks. Let’s denote the goal level of entities as ScIn (Scope
Information). The purpose of information frameworks is to generalize knowledge, which is presented in the form of a
set of heterogeneous entities and is actually a semantic representation of one and the same element of the surrounding
world. At the level of the concept, the presence of heterogeneity in the presentation of information in the sources is
assumed. Heterogeneity becomes the basis of the method and is not a problem since it practically manifests itself within
the boundaries of the corpus and, therefore, is known. Thus, the heterogeneity is limited by the corpus. The information
framework is initially unknown, although it is defined by knowledge and limited by the corpus.
We define the formal model of the information framework as follows
, ( ), .ScIn Ent Prop Des Scp= (1)
Here Ent - a set of named entities; ( )Prop Des - a set of properties that form named entities and are determined
by a set of descriptions; Scp - the range of properties is defined by a tuple.
( ) , ,Scp ent Scp ent ent Ent ent є сутністю ScIn= (2)
If defined, the framework of properties in relation to the entity
( ) Scp ent prop Prop prop є властивістю ent= (3)
The diversity of language DivLang is formalized in the presence of a set of named entities. Named entities can
formalize the same knowledge by a set of properties but have different names.
The diversity of knowledge DivKnow is formalized by quantizing knowledge in the form of properties
( )Prop Des . The same properties, for example, can form different named entities forming a non-empty intersection of
properties belonging to certain entities.
We will present the diversity of the language in the form of such concepts as synonyms and hyperonyms.
Linguistic correspondence at the level of synonyms
( , )
. . ( , ) ( , )
( , )synonym synonym ent ent
s t synonym ent en
m
n
atch en
y
t ent
t sy on m ent ent
=
(4)
To find synonyms, i.e., entities that have different nominal designations and a fully coincident set of property
descriptions defined ( ) ( ) , , ,ent ent ent O ent Ds ent entdsc dsc dsc Dsc dsc Dsc dsc dsc dsc .
Linguistic correspondence at the level of hyperonyms
( , )
. . ( , ) ( , )
( , )hyperonym hyperonym ent ent
s t hyperonym ent en
m
e
atch en
n
t ent
t hyp ro ym ent ent
=
(5)
(5)
To find hyperonyms, that is, entities, the relationship between which is that one entity is more general
than the other. Taken in relation to descriptions, the entity that is a hyperonym is above the plural relative to an-
other entity. defined in this way
Інформаційні системи
To find hyperonyms, that is, entities, the relationship between which is that one entity is more general than the
other. Taken in relation to descriptions, the entity that is a hyperonym is above the plural relative to another entity.
defined in this way ent O ent Dsdsc ent Ent dsc ent Ent , the inverse relation is also defined
ent Ds ent Odsc ent Ent dsc ent Ent .
Within the framework of the structured domain, linguistic heterogeneity is defined by codes relating synonyms
and hyperonyms to ethical entities. Such diversity is limited to diversity within named designations. Further research is
conducted within the framework of the diversity of knowledge representation. However, given that the knowledge
representation within the corpus is linguistic, establishing linguistic correspondence at the description level dsc is an
important step and is performed for each description. The most important and desirable thing is to establish the
descriptions according to the correspondence of the synonyms of the descriptions synonymmatch because such
correspondence is maximal. Hyperonym matching hyperonymmatch is the next step if no synonym matches are found. The
matching of hyperonyms requires the establishment of a threshold value for taking into account the matching and
measure of content. If the measure of content is too small, that is, the hyperonym too generalizes a description from
another domain of comparison, such a relation is considered inappropriate.
Semantic alignment is defined by the function ( )semantic . Next, we will define a descriptive definition of the
matching method.
A quantized unit of knowledge is obtained based on descriptions Des from the Knowledge Base. Knowledge
Base is limited to the body of documents and is formed by it. In the diversity of knowledge, certain knowledge is
formed by separate descriptions and their aggregates and is formally presented in the form of properties ( )Prop Des .
Thus, the central place in semantic heterogeneity is occupied by knowledge, which can have a different form of
representation, both structural and linguistic, but unchanged in the sense of informativeness. The body of knowledge is
grouped in the form of a certain association. The association is grouped formally in the form of informative
frameworks, which represent a certain conceptual structure, the purpose of which, in the case of semantic comparison,
is to correspond to the essence of the basic set of the ontology of trust. Note that in the basic set, the entity itself is
compared as a fixed and formed element. This entity is formed by the existing informative framework of compliance on
the side of the structured domain. That is, the correspondence is determined by the set of knowledge presented in the
form of formalized properties within the information scope. The unit of comparison is the informative frame, that is, the
unit of the meta-level above the entities of the structured domain. Let’s depict a graphic representation of the formation
of an information scope element (Figure 1).
Properties are formed from the knowledge base by extracting individual descriptions. A set of properties that can
be generalized by common informativeness forms an entity and is a subset of properties
ient
prop Prop . A set of
entities that can be summarized by common informativeness at the entity level form an informative scope and are a
subset of entities
iscin
ent Ent . Joint informativeness at the level of entities in relation to an information scope can
be bound by a partial relation, since it may not fully belong to the feature of generalization and refer to an information
scope. That is, not all properties that form a certain entity can be included in the information scope.
, the inverse relation is also defined
Інформаційні системи
To find hyperonyms, that is, entities, the relationship between which is that one entity is more general than the
other. Taken in relation to descriptions, the entity that is a hyperonym is above the plural relative to another entity.
defined in this way ent O ent Dsdsc ent Ent dsc ent Ent , the inverse relation is also defined
ent Ds ent Odsc ent Ent dsc ent Ent .
Within the framework of the structured domain, linguistic heterogeneity is defined by codes relating synonyms
and hyperonyms to ethical entities. Such diversity is limited to diversity within named designations. Further research is
conducted within the framework of the diversity of knowledge representation. However, given that the knowledge
representation within the corpus is linguistic, establishing linguistic correspondence at the description level dsc is an
important step and is performed for each description. The most important and desirable thing is to establish the
descriptions according to the correspondence of the synonyms of the descriptions synonymmatch because such
correspondence is maximal. Hyperonym matching hyperonymmatch is the next step if no synonym matches are found. The
matching of hyperonyms requires the establishment of a threshold value for taking into account the matching and
measure of content. If the measure of content is too small, that is, the hyperonym too generalizes a description from
another domain of comparison, such a relation is considered inappropriate.
Semantic alignment is defined by the function ( )semantic . Next, we will define a descriptive definition of the
matching method.
A quantized unit of knowledge is obtained based on descriptions Des from the Knowledge Base. Knowledge
Base is limited to the body of documents and is formed by it. In the diversity of knowledge, certain knowledge is
formed by separate descriptions and their aggregates and is formally presented in the form of properties ( )Prop Des .
Thus, the central place in semantic heterogeneity is occupied by knowledge, which can have a different form of
representation, both structural and linguistic, but unchanged in the sense of informativeness. The body of knowledge is
grouped in the form of a certain association. The association is grouped formally in the form of informative
frameworks, which represent a certain conceptual structure, the purpose of which, in the case of semantic comparison,
is to correspond to the essence of the basic set of the ontology of trust. Note that in the basic set, the entity itself is
compared as a fixed and formed element. This entity is formed by the existing informative framework of compliance on
the side of the structured domain. That is, the correspondence is determined by the set of knowledge presented in the
form of formalized properties within the information scope. The unit of comparison is the informative frame, that is, the
unit of the meta-level above the entities of the structured domain. Let’s depict a graphic representation of the formation
of an information scope element (Figure 1).
Properties are formed from the knowledge base by extracting individual descriptions. A set of properties that can
be generalized by common informativeness forms an entity and is a subset of properties
ient
prop Prop . A set of
entities that can be summarized by common informativeness at the entity level form an informative scope and are a
subset of entities
iscin
ent Ent . Joint informativeness at the level of entities in relation to an information scope can
be bound by a partial relation, since it may not fully belong to the feature of generalization and refer to an information
scope. That is, not all properties that form a certain entity can be included in the information scope.
.
Within the framework of the structured domain, linguistic heterogeneity is defined by codes relating synonyms
and hyperonyms to ethical entities. Such diversity is limited to diversity within named designations. Further research
is conducted within the framework of the diversity of knowledge representation. However, given that the knowl-
edge representation within the corpus is linguistic, establishing linguistic correspondence at the description
level
Інформаційні системи
To find hyperonyms, that is, entities, the relationship between which is that one entity is more general than the
other. Taken in relation to descriptions, the entity that is a hyperonym is above the plural relative to another entity.
defined in this way ent O ent Dsdsc ent Ent dsc ent Ent , the inverse relation is also defined
ent Ds ent Odsc ent Ent dsc ent Ent .
Within the framework of the structured domain, linguistic heterogeneity is defined by codes relating synonyms
and hyperonyms to ethical entities. Such diversity is limited to diversity within named designations. Further research is
conducted within the framework of the diversity of knowledge representation. However, given that the knowledge
representation within the corpus is linguistic, establishing linguistic correspondence at the description level dsc is an
important step and is performed for each description. The most important and desirable thing is to establish the
descriptions according to the correspondence of the synonyms of the descriptions synonymmatch because such
correspondence is maximal. Hyperonym matching hyperonymmatch is the next step if no synonym matches are found. The
matching of hyperonyms requires the establishment of a threshold value for taking into account the matching and
measure of content. If the measure of content is too small, that is, the hyperonym too generalizes a description from
another domain of comparison, such a relation is considered inappropriate.
Semantic alignment is defined by the function ( )semantic . Next, we will define a descriptive definition of the
matching method.
A quantized unit of knowledge is obtained based on descriptions Des from the Knowledge Base. Knowledge
Base is limited to the body of documents and is formed by it. In the diversity of knowledge, certain knowledge is
formed by separate descriptions and their aggregates and is formally presented in the form of properties ( )Prop Des .
Thus, the central place in semantic heterogeneity is occupied by knowledge, which can have a different form of
representation, both structural and linguistic, but unchanged in the sense of informativeness. The body of knowledge is
grouped in the form of a certain association. The association is grouped formally in the form of informative
frameworks, which represent a certain conceptual structure, the purpose of which, in the case of semantic comparison,
is to correspond to the essence of the basic set of the ontology of trust. Note that in the basic set, the entity itself is
compared as a fixed and formed element. This entity is formed by the existing informative framework of compliance on
the side of the structured domain. That is, the correspondence is determined by the set of knowledge presented in the
form of formalized properties within the information scope. The unit of comparison is the informative frame, that is, the
unit of the meta-level above the entities of the structured domain. Let’s depict a graphic representation of the formation
of an information scope element (Figure 1).
Properties are formed from the knowledge base by extracting individual descriptions. A set of properties that can
be generalized by common informativeness forms an entity and is a subset of properties
ient
prop Prop . A set of
entities that can be summarized by common informativeness at the entity level form an informative scope and are a
subset of entities
iscin
ent Ent . Joint informativeness at the level of entities in relation to an information scope can
be bound by a partial relation, since it may not fully belong to the feature of generalization and refer to an information
scope. That is, not all properties that form a certain entity can be included in the information scope.
is an important step and is performed for each description. The most important and desirable thing is
to establish the descriptions according to the correspondence of the synonyms of the descriptions
Інформаційні системи
To find hyperonyms, that is, entities, the relationship between which is that one entity is more general than the
other. Taken in relation to descriptions, the entity that is a hyperonym is above the plural relative to another entity.
defined in this way ent O ent Dsdsc ent Ent dsc ent Ent , the inverse relation is also defined
ent Ds ent Odsc ent Ent dsc ent Ent .
Within the framework of the structured domain, linguistic heterogeneity is defined by codes relating synonyms
and hyperonyms to ethical entities. Such diversity is limited to diversity within named designations. Further research is
conducted within the framework of the diversity of knowledge representation. However, given that the knowledge
representation within the corpus is linguistic, establishing linguistic correspondence at the description level dsc is an
important step and is performed for each description. The most important and desirable thing is to establish the
descriptions according to the correspondence of the synonyms of the descriptions synonymmatch because such
correspondence is maximal. Hyperonym matching hyperonymmatch is the next step if no synonym matches are found. The
matching of hyperonyms requires the establishment of a threshold value for taking into account the matching and
measure of content. If the measure of content is too small, that is, the hyperonym too generalizes a description from
another domain of comparison, such a relation is considered inappropriate.
Semantic alignment is defined by the function ( )semantic . Next, we will define a descriptive definition of the
matching method.
A quantized unit of knowledge is obtained based on descriptions Des from the Knowledge Base. Knowledge
Base is limited to the body of documents and is formed by it. In the diversity of knowledge, certain knowledge is
formed by separate descriptions and their aggregates and is formally presented in the form of properties ( )Prop Des .
Thus, the central place in semantic heterogeneity is occupied by knowledge, which can have a different form of
representation, both structural and linguistic, but unchanged in the sense of informativeness. The body of knowledge is
grouped in the form of a certain association. The association is grouped formally in the form of informative
frameworks, which represent a certain conceptual structure, the purpose of which, in the case of semantic comparison,
is to correspond to the essence of the basic set of the ontology of trust. Note that in the basic set, the entity itself is
compared as a fixed and formed element. This entity is formed by the existing informative framework of compliance on
the side of the structured domain. That is, the correspondence is determined by the set of knowledge presented in the
form of formalized properties within the information scope. The unit of comparison is the informative frame, that is, the
unit of the meta-level above the entities of the structured domain. Let’s depict a graphic representation of the formation
of an information scope element (Figure 1).
Properties are formed from the knowledge base by extracting individual descriptions. A set of properties that can
be generalized by common informativeness forms an entity and is a subset of properties
ient
prop Prop . A set of
entities that can be summarized by common informativeness at the entity level form an informative scope and are a
subset of entities
iscin
ent Ent . Joint informativeness at the level of entities in relation to an information scope can
be bound by a partial relation, since it may not fully belong to the feature of generalization and refer to an information
scope. That is, not all properties that form a certain entity can be included in the information scope.
because such correspondence is maximal. Hyperonym matching
Інформаційні системи
To find hyperonyms, that is, entities, the relationship between which is that one entity is more general than the
other. Taken in relation to descriptions, the entity that is a hyperonym is above the plural relative to another entity.
defined in this way ent O ent Dsdsc ent Ent dsc ent Ent , the inverse relation is also defined
ent Ds ent Odsc ent Ent dsc ent Ent .
Within the framework of the structured domain, linguistic heterogeneity is defined by codes relating synonyms
and hyperonyms to ethical entities. Such diversity is limited to diversity within named designations. Further research is
conducted within the framework of the diversity of knowledge representation. However, given that the knowledge
representation within the corpus is linguistic, establishing linguistic correspondence at the description level dsc is an
important step and is performed for each description. The most important and desirable thing is to establish the
descriptions according to the correspondence of the synonyms of the descriptions synonymmatch because such
correspondence is maximal. Hyperonym matching hyperonymmatch is the next step if no synonym matches are found. The
matching of hyperonyms requires the establishment of a threshold value for taking into account the matching and
measure of content. If the measure of content is too small, that is, the hyperonym too generalizes a description from
another domain of comparison, such a relation is considered inappropriate.
Semantic alignment is defined by the function ( )semantic . Next, we will define a descriptive definition of the
matching method.
A quantized unit of knowledge is obtained based on descriptions Des from the Knowledge Base. Knowledge
Base is limited to the body of documents and is formed by it. In the diversity of knowledge, certain knowledge is
formed by separate descriptions and their aggregates and is formally presented in the form of properties ( )Prop Des .
Thus, the central place in semantic heterogeneity is occupied by knowledge, which can have a different form of
representation, both structural and linguistic, but unchanged in the sense of informativeness. The body of knowledge is
grouped in the form of a certain association. The association is grouped formally in the form of informative
frameworks, which represent a certain conceptual structure, the purpose of which, in the case of semantic comparison,
is to correspond to the essence of the basic set of the ontology of trust. Note that in the basic set, the entity itself is
compared as a fixed and formed element. This entity is formed by the existing informative framework of compliance on
the side of the structured domain. That is, the correspondence is determined by the set of knowledge presented in the
form of formalized properties within the information scope. The unit of comparison is the informative frame, that is, the
unit of the meta-level above the entities of the structured domain. Let’s depict a graphic representation of the formation
of an information scope element (Figure 1).
Properties are formed from the knowledge base by extracting individual descriptions. A set of properties that can
be generalized by common informativeness forms an entity and is a subset of properties
ient
prop Prop . A set of
entities that can be summarized by common informativeness at the entity level form an informative scope and are a
subset of entities
iscin
ent Ent . Joint informativeness at the level of entities in relation to an information scope can
be bound by a partial relation, since it may not fully belong to the feature of generalization and refer to an information
scope. That is, not all properties that form a certain entity can be included in the information scope.
is the next step if no synonym
matches are found. The matching of hyperonyms requires the establishment of a threshold value for taking into
account the matching and measure of content. If the measure of content is too small, that is, the hyperonym
too generalizes a description from another domain of comparison, such a relation is considered inappropriate.
Semantic alignment is defined by the function
Інформаційні системи
To find hyperonyms, that is, entities, the relationship between which is that one entity is more general than the
other. Taken in relation to descriptions, the entity that is a hyperonym is above the plural relative to another entity.
defined in this way ent O ent Dsdsc ent Ent dsc ent Ent , the inverse relation is also defined
ent Ds ent Odsc ent Ent dsc ent Ent .
Within the framework of the structured domain, linguistic heterogeneity is defined by codes relating synonyms
and hyperonyms to ethical entities. Such diversity is limited to diversity within named designations. Further research is
conducted within the framework of the diversity of knowledge representation. However, given that the knowledge
representation within the corpus is linguistic, establishing linguistic correspondence at the description level dsc is an
important step and is performed for each description. The most important and desirable thing is to establish the
descriptions according to the correspondence of the synonyms of the descriptions synonymmatch because such
correspondence is maximal. Hyperonym matching hyperonymmatch is the next step if no synonym matches are found. The
matching of hyperonyms requires the establishment of a threshold value for taking into account the matching and
measure of content. If the measure of content is too small, that is, the hyperonym too generalizes a description from
another domain of comparison, such a relation is considered inappropriate.
Semantic alignment is defined by the function ( )semantic . Next, we will define a descriptive definition of the
matching method.
A quantized unit of knowledge is obtained based on descriptions Des from the Knowledge Base. Knowledge
Base is limited to the body of documents and is formed by it. In the diversity of knowledge, certain knowledge is
formed by separate descriptions and their aggregates and is formally presented in the form of properties ( )Prop Des .
Thus, the central place in semantic heterogeneity is occupied by knowledge, which can have a different form of
representation, both structural and linguistic, but unchanged in the sense of informativeness. The body of knowledge is
grouped in the form of a certain association. The association is grouped formally in the form of informative
frameworks, which represent a certain conceptual structure, the purpose of which, in the case of semantic comparison,
is to correspond to the essence of the basic set of the ontology of trust. Note that in the basic set, the entity itself is
compared as a fixed and formed element. This entity is formed by the existing informative framework of compliance on
the side of the structured domain. That is, the correspondence is determined by the set of knowledge presented in the
form of formalized properties within the information scope. The unit of comparison is the informative frame, that is, the
unit of the meta-level above the entities of the structured domain. Let’s depict a graphic representation of the formation
of an information scope element (Figure 1).
Properties are formed from the knowledge base by extracting individual descriptions. A set of properties that can
be generalized by common informativeness forms an entity and is a subset of properties
ient
prop Prop . A set of
entities that can be summarized by common informativeness at the entity level form an informative scope and are a
subset of entities
iscin
ent Ent . Joint informativeness at the level of entities in relation to an information scope can
be bound by a partial relation, since it may not fully belong to the feature of generalization and refer to an information
scope. That is, not all properties that form a certain entity can be included in the information scope.
. Next, we will define a descriptive defini-
tion of the matching method.
358
Інформаційні системи
A quantized unit of knowledge is obtained based on descriptions
Інформаційні системи
To find hyperonyms, that is, entities, the relationship between which is that one entity is more general than the
other. Taken in relation to descriptions, the entity that is a hyperonym is above the plural relative to another entity.
defined in this way ent O ent Dsdsc ent Ent dsc ent Ent , the inverse relation is also defined
ent Ds ent Odsc ent Ent dsc ent Ent .
Within the framework of the structured domain, linguistic heterogeneity is defined by codes relating synonyms
and hyperonyms to ethical entities. Such diversity is limited to diversity within named designations. Further research is
conducted within the framework of the diversity of knowledge representation. However, given that the knowledge
representation within the corpus is linguistic, establishing linguistic correspondence at the description level dsc is an
important step and is performed for each description. The most important and desirable thing is to establish the
descriptions according to the correspondence of the synonyms of the descriptions synonymmatch because such
correspondence is maximal. Hyperonym matching hyperonymmatch is the next step if no synonym matches are found. The
matching of hyperonyms requires the establishment of a threshold value for taking into account the matching and
measure of content. If the measure of content is too small, that is, the hyperonym too generalizes a description from
another domain of comparison, such a relation is considered inappropriate.
Semantic alignment is defined by the function ( )semantic . Next, we will define a descriptive definition of the
matching method.
A quantized unit of knowledge is obtained based on descriptions Des from the Knowledge Base. Knowledge
Base is limited to the body of documents and is formed by it. In the diversity of knowledge, certain knowledge is
formed by separate descriptions and their aggregates and is formally presented in the form of properties ( )Prop Des .
Thus, the central place in semantic heterogeneity is occupied by knowledge, which can have a different form of
representation, both structural and linguistic, but unchanged in the sense of informativeness. The body of knowledge is
grouped in the form of a certain association. The association is grouped formally in the form of informative
frameworks, which represent a certain conceptual structure, the purpose of which, in the case of semantic comparison,
is to correspond to the essence of the basic set of the ontology of trust. Note that in the basic set, the entity itself is
compared as a fixed and formed element. This entity is formed by the existing informative framework of compliance on
the side of the structured domain. That is, the correspondence is determined by the set of knowledge presented in the
form of formalized properties within the information scope. The unit of comparison is the informative frame, that is, the
unit of the meta-level above the entities of the structured domain. Let’s depict a graphic representation of the formation
of an information scope element (Figure 1).
Properties are formed from the knowledge base by extracting individual descriptions. A set of properties that can
be generalized by common informativeness forms an entity and is a subset of properties
ient
prop Prop . A set of
entities that can be summarized by common informativeness at the entity level form an informative scope and are a
subset of entities
iscin
ent Ent . Joint informativeness at the level of entities in relation to an information scope can
be bound by a partial relation, since it may not fully belong to the feature of generalization and refer to an information
scope. That is, not all properties that form a certain entity can be included in the information scope.
from the Knowledge Base.
Knowledge Base is limited to the body of documents and is formed by it. In the diversity of knowledge, certain
knowledge is formed by separate descriptions and their aggregates and is formally presented in the form of
properties
Інформаційні системи
To find hyperonyms, that is, entities, the relationship between which is that one entity is more general than the
other. Taken in relation to descriptions, the entity that is a hyperonym is above the plural relative to another entity.
defined in this way ent O ent Dsdsc ent Ent dsc ent Ent , the inverse relation is also defined
ent Ds ent Odsc ent Ent dsc ent Ent .
Within the framework of the structured domain, linguistic heterogeneity is defined by codes relating synonyms
and hyperonyms to ethical entities. Such diversity is limited to diversity within named designations. Further research is
conducted within the framework of the diversity of knowledge representation. However, given that the knowledge
representation within the corpus is linguistic, establishing linguistic correspondence at the description level dsc is an
important step and is performed for each description. The most important and desirable thing is to establish the
descriptions according to the correspondence of the synonyms of the descriptions synonymmatch because such
correspondence is maximal. Hyperonym matching hyperonymmatch is the next step if no synonym matches are found. The
matching of hyperonyms requires the establishment of a threshold value for taking into account the matching and
measure of content. If the measure of content is too small, that is, the hyperonym too generalizes a description from
another domain of comparison, such a relation is considered inappropriate.
Semantic alignment is defined by the function ( )semantic . Next, we will define a descriptive definition of the
matching method.
A quantized unit of knowledge is obtained based on descriptions Des from the Knowledge Base. Knowledge
Base is limited to the body of documents and is formed by it. In the diversity of knowledge, certain knowledge is
formed by separate descriptions and their aggregates and is formally presented in the form of properties ( )Prop Des .
Thus, the central place in semantic heterogeneity is occupied by knowledge, which can have a different form of
representation, both structural and linguistic, but unchanged in the sense of informativeness. The body of knowledge is
grouped in the form of a certain association. The association is grouped formally in the form of informative
frameworks, which represent a certain conceptual structure, the purpose of which, in the case of semantic comparison,
is to correspond to the essence of the basic set of the ontology of trust. Note that in the basic set, the entity itself is
compared as a fixed and formed element. This entity is formed by the existing informative framework of compliance on
the side of the structured domain. That is, the correspondence is determined by the set of knowledge presented in the
form of formalized properties within the information scope. The unit of comparison is the informative frame, that is, the
unit of the meta-level above the entities of the structured domain. Let’s depict a graphic representation of the formation
of an information scope element (Figure 1).
Properties are formed from the knowledge base by extracting individual descriptions. A set of properties that can
be generalized by common informativeness forms an entity and is a subset of properties
ient
prop Prop . A set of
entities that can be summarized by common informativeness at the entity level form an informative scope and are a
subset of entities
iscin
ent Ent . Joint informativeness at the level of entities in relation to an information scope can
be bound by a partial relation, since it may not fully belong to the feature of generalization and refer to an information
scope. That is, not all properties that form a certain entity can be included in the information scope.
.
Thus, the central place in semantic heterogeneity is occupied by knowledge, which can have a dif-
ferent form of representation, both structural and linguistic, but unchanged in the sense of informativeness.
The body of knowledge is grouped in the form of a certain association. The association is grouped formally
in the form of informative frameworks, which represent a certain conceptual structure, the purpose of which,
in the case of semantic comparison, is to correspond to the essence of the basic set of the ontology of trust.
Note that in the basic set, the entity itself is compared as a fixed and formed element. This entity is formed
by the existing informative framework of compliance on the side of the structured domain. That is, the cor-
respondence is determined by the set of knowledge presented in the form of formalized properties within the
information scope. The unit of comparison is the informative frame, that is, the unit of the meta-level above
the entities of the structured domain. Let’s depict a graphic representation of the formation of an information
scope element (Figure 1). Інформаційні системи
[Введите текст]
Figure 1 - Formation of an element of an information scope ScIn in the form of aggregate properties
as a function of descriptions ( )Prop Dsc obtained from the knowledge base
Thus, the information scope is a certain meta entity that can, in some cases, summarize several defined entities in
the domain Ds or partially correlate with other entities at the level of properties; however, it is possible to match the
research entity from the domain as much as possible in terms of the degree of correspondence O . This corresponds to
the main purpose of its formation according to the semantic method of comparison.
An entity is not a simple generalization or formal naming of a subset of properties. An entity is a generalization
of knowledge and informativeness over a subset of properties, considering the relationships between entities. The same
ratio connects the concepts of essence and information scope. At the same time, at the generalization level, entities can
broadcast the generalization of knowledge and informativeness to the information scope through the generalization of
properties. This relationship structure is depicted in Figure 2.
Thus, an information scope is used for comparison, as a generalization of knowledge at the level of properties
Ds Oscin ent . The set of properties is summarized by an information scope in such a way as to correspond to a certain
entity from the ontology of trust as much as possible. This method isolates knowledge to determine the degree of
alignment.
This allows for a flexible alignment method. The need for the presented model of establishing the degree of
correspondence is justified by the fact that the named entities on the set of the structured domain do not always and do
not fully correspond to the entities from the ontology according to meaningful criteria. Accordingly, to ensure a
qualitative comparison and considering the semantic heterogeneity of corpus documents, a meta-entity is formed on the
side of the structured domain, the main purpose of which is to maximally correspond to the entity on the ontology side
in terms of content.
Thus, the method is also justified by the fact that the structured domain is less formalized than the ontology and
has greater heterogeneity of both language and knowledge representation. This gives more opportunities for searching
Figure 1. Formation of an element of an information scope
Інформаційні системи
[Введите текст]
Figure 1 - Formation of an element of an information scope ScIn in the form of aggregate properties
as a function of descriptions ( )Prop Dsc obtained from the knowledge base
Thus, the information scope is a certain meta entity that can, in some cases, summarize several defined entities in
the domain Ds or partially correlate with other entities at the level of properties; however, it is possible to match the
research entity from the domain as much as possible in terms of the degree of correspondence O . This corresponds to
the main purpose of its formation according to the semantic method of comparison.
An entity is not a simple generalization or formal naming of a subset of properties. An entity is a generalization
of knowledge and informativeness over a subset of properties, considering the relationships between entities. The same
ratio connects the concepts of essence and information scope. At the same time, at the generalization level, entities can
broadcast the generalization of knowledge and informativeness to the information scope through the generalization of
properties. This relationship structure is depicted in Figure 2.
Thus, an information scope is used for comparison, as a generalization of knowledge at the level of properties
Ds Oscin ent . The set of properties is summarized by an information scope in such a way as to correspond to a certain
entity from the ontology of trust as much as possible. This method isolates knowledge to determine the degree of
alignment.
This allows for a flexible alignment method. The need for the presented model of establishing the degree of
correspondence is justified by the fact that the named entities on the set of the structured domain do not always and do
not fully correspond to the entities from the ontology according to meaningful criteria. Accordingly, to ensure a
qualitative comparison and considering the semantic heterogeneity of corpus documents, a meta-entity is formed on the
side of the structured domain, the main purpose of which is to maximally correspond to the entity on the ontology side
in terms of content.
Thus, the method is also justified by the fact that the structured domain is less formalized than the ontology and
has greater heterogeneity of both language and knowledge representation. This gives more opportunities for searching
in the form of aggregate
properties as a function of descriptions
Інформаційні системи
[Введите текст]
Figure 1 - Formation of an element of an information scope ScIn in the form of aggregate properties
as a function of descriptions ( )Prop Dsc obtained from the knowledge base
Thus, the information scope is a certain meta entity that can, in some cases, summarize several defined entities in
the domain Ds or partially correlate with other entities at the level of properties; however, it is possible to match the
research entity from the domain as much as possible in terms of the degree of correspondence O . This corresponds to
the main purpose of its formation according to the semantic method of comparison.
An entity is not a simple generalization or formal naming of a subset of properties. An entity is a generalization
of knowledge and informativeness over a subset of properties, considering the relationships between entities. The same
ratio connects the concepts of essence and information scope. At the same time, at the generalization level, entities can
broadcast the generalization of knowledge and informativeness to the information scope through the generalization of
properties. This relationship structure is depicted in Figure 2.
Thus, an information scope is used for comparison, as a generalization of knowledge at the level of properties
Ds Oscin ent . The set of properties is summarized by an information scope in such a way as to correspond to a certain
entity from the ontology of trust as much as possible. This method isolates knowledge to determine the degree of
alignment.
This allows for a flexible alignment method. The need for the presented model of establishing the degree of
correspondence is justified by the fact that the named entities on the set of the structured domain do not always and do
not fully correspond to the entities from the ontology according to meaningful criteria. Accordingly, to ensure a
qualitative comparison and considering the semantic heterogeneity of corpus documents, a meta-entity is formed on the
side of the structured domain, the main purpose of which is to maximally correspond to the entity on the ontology side
in terms of content.
Thus, the method is also justified by the fact that the structured domain is less formalized than the ontology and
has greater heterogeneity of both language and knowledge representation. This gives more opportunities for searching
obtained from the knowledge base
359
Інформаційні системи
Properties are formed from the knowledge base by extracting individual descriptions. A set of prop-
erties that can be generalized by common informativeness forms an entity and is a subset of properties
Інформаційні системи
To find hyperonyms, that is, entities, the relationship between which is that one entity is more general than the
other. Taken in relation to descriptions, the entity that is a hyperonym is above the plural relative to another entity.
defined in this way ent O ent Dsdsc ent Ent dsc ent Ent , the inverse relation is also defined
ent Ds ent Odsc ent Ent dsc ent Ent .
Within the framework of the structured domain, linguistic heterogeneity is defined by codes relating synonyms
and hyperonyms to ethical entities. Such diversity is limited to diversity within named designations. Further research is
conducted within the framework of the diversity of knowledge representation. However, given that the knowledge
representation within the corpus is linguistic, establishing linguistic correspondence at the description level dsc is an
important step and is performed for each description. The most important and desirable thing is to establish the
descriptions according to the correspondence of the synonyms of the descriptions synonymmatch because such
correspondence is maximal. Hyperonym matching hyperonymmatch is the next step if no synonym matches are found. The
matching of hyperonyms requires the establishment of a threshold value for taking into account the matching and
measure of content. If the measure of content is too small, that is, the hyperonym too generalizes a description from
another domain of comparison, such a relation is considered inappropriate.
Semantic alignment is defined by the function ( )semantic . Next, we will define a descriptive definition of the
matching method.
A quantized unit of knowledge is obtained based on descriptions Des from the Knowledge Base. Knowledge
Base is limited to the body of documents and is formed by it. In the diversity of knowledge, certain knowledge is
formed by separate descriptions and their aggregates and is formally presented in the form of properties ( )Prop Des .
Thus, the central place in semantic heterogeneity is occupied by knowledge, which can have a different form of
representation, both structural and linguistic, but unchanged in the sense of informativeness. The body of knowledge is
grouped in the form of a certain association. The association is grouped formally in the form of informative
frameworks, which represent a certain conceptual structure, the purpose of which, in the case of semantic comparison,
is to correspond to the essence of the basic set of the ontology of trust. Note that in the basic set, the entity itself is
compared as a fixed and formed element. This entity is formed by the existing informative framework of compliance on
the side of the structured domain. That is, the correspondence is determined by the set of knowledge presented in the
form of formalized properties within the information scope. The unit of comparison is the informative frame, that is, the
unit of the meta-level above the entities of the structured domain. Let’s depict a graphic representation of the formation
of an information scope element (Figure 1).
Properties are formed from the knowledge base by extracting individual descriptions. A set of properties that can
be generalized by common informativeness forms an entity and is a subset of properties
ient
prop Prop . A set of
entities that can be summarized by common informativeness at the entity level form an informative scope and are a
subset of entities
iscin
ent Ent . Joint informativeness at the level of entities in relation to an information scope can
be bound by a partial relation, since it may not fully belong to the feature of generalization and refer to an information
scope. That is, not all properties that form a certain entity can be included in the information scope.
. A set of entities that can be summarized by common informativeness at the entity level
form an informative scope and are a subset of entities
Інформаційні системи
To find hyperonyms, that is, entities, the relationship between which is that one entity is more general than the
other. Taken in relation to descriptions, the entity that is a hyperonym is above the plural relative to another entity.
defined in this way ent O ent Dsdsc ent Ent dsc ent Ent , the inverse relation is also defined
ent Ds ent Odsc ent Ent dsc ent Ent .
Within the framework of the structured domain, linguistic heterogeneity is defined by codes relating synonyms
and hyperonyms to ethical entities. Such diversity is limited to diversity within named designations. Further research is
conducted within the framework of the diversity of knowledge representation. However, given that the knowledge
representation within the corpus is linguistic, establishing linguistic correspondence at the description level dsc is an
important step and is performed for each description. The most important and desirable thing is to establish the
descriptions according to the correspondence of the synonyms of the descriptions synonymmatch because such
correspondence is maximal. Hyperonym matching hyperonymmatch is the next step if no synonym matches are found. The
matching of hyperonyms requires the establishment of a threshold value for taking into account the matching and
measure of content. If the measure of content is too small, that is, the hyperonym too generalizes a description from
another domain of comparison, such a relation is considered inappropriate.
Semantic alignment is defined by the function ( )semantic . Next, we will define a descriptive definition of the
matching method.
A quantized unit of knowledge is obtained based on descriptions Des from the Knowledge Base. Knowledge
Base is limited to the body of documents and is formed by it. In the diversity of knowledge, certain knowledge is
formed by separate descriptions and their aggregates and is formally presented in the form of properties ( )Prop Des .
Thus, the central place in semantic heterogeneity is occupied by knowledge, which can have a different form of
representation, both structural and linguistic, but unchanged in the sense of informativeness. The body of knowledge is
grouped in the form of a certain association. The association is grouped formally in the form of informative
frameworks, which represent a certain conceptual structure, the purpose of which, in the case of semantic comparison,
is to correspond to the essence of the basic set of the ontology of trust. Note that in the basic set, the entity itself is
compared as a fixed and formed element. This entity is formed by the existing informative framework of compliance on
the side of the structured domain. That is, the correspondence is determined by the set of knowledge presented in the
form of formalized properties within the information scope. The unit of comparison is the informative frame, that is, the
unit of the meta-level above the entities of the structured domain. Let’s depict a graphic representation of the formation
of an information scope element (Figure 1).
Properties are formed from the knowledge base by extracting individual descriptions. A set of properties that can
be generalized by common informativeness forms an entity and is a subset of properties
ient
prop Prop . A set of
entities that can be summarized by common informativeness at the entity level form an informative scope and are a
subset of entities
iscin
ent Ent . Joint informativeness at the level of entities in relation to an information scope can
be bound by a partial relation, since it may not fully belong to the feature of generalization and refer to an information
scope. That is, not all properties that form a certain entity can be included in the information scope.
. Joint informativeness at the level of
entities in relation to an information scope can be bound by a partial relation, since it may not fully belong to
the feature of generalization and refer to an information scope. That is, not all properties that form a certain
entity can be included in the information scope.
Thus, the information scope is a certain meta entity that can, in some cases, summarize several defined
entities in the domain Ds or partially correlate with other entities at the level of properties; however, it is pos-
sible to match the research entity from the domain as much as possible in terms of the degree of correspondence
O. This corresponds to the main purpose of its formation according to the semantic method of comparison.
An entity is not a simple generalization or formal naming of a subset of properties. An entity is a gener-
alization of knowledge and informativeness over a subset of properties, considering the relationships between
entities. The same ratio connects the concepts of essence and information scope. At the same time, at the gen-
eralization level, entities can broadcast the generalization of knowledge and informativeness to the information
scope through the generalization of properties. This relationship structure is depicted in Figure 2.
Інформаційні системи
and forming generalizations for correspondences, and if necessary and insufficient informativeness of the structured
domain, the original source of information from the corpus of documents is obtained. The decision to establish
compliance is formed using the descriptions of the corpus documents. The description of the relevant property is taken
from a certain set of documents to ensure objectivity and diversity of views.
Figure 2 – The main variants of the presentation form of the information scope
The main variants of the form of presentation of the informative frame, shown in Figure 2, can be used in
combination. Option 9 is not implemented because the descriptor is not a complete representation of the content. The
totality of the set of descriptors 1
ndcs that form property 1
nprop dcs= is represented by option 8. This is the
minimal form of formation of an informative framework with the definition of content min 1: nscin scin prop dcs= = .
Other options allow expansion when an informative frame is formed by expanding the basic formation.
Thus, we define correspondence for semantic alignment as follows
( )
( )( ) ( )( )
( )
,
, Ds Ds semanticPropertie O Ds ent Oprop Prop Fun prop prop threshhold
ent O
a
Prop
entsem ntic ent scin
Prop
= (6)
The semantic alignment function is defined using the semantic alignment of properties and is a function of the
set of descriptions dsc that are obtained from the ontology domain dom O and the structured domain of the corpus
dom Ds , respectively.
Figure 2. The main variants of the presentation form of the information scope
Thus, an information scope is used for comparison, as a generalization of knowledge at the level of
properties
Інформаційні системи
[Введите текст]
Figure 1 - Formation of an element of an information scope ScIn in the form of aggregate properties
as a function of descriptions ( )Prop Dsc obtained from the knowledge base
Thus, the information scope is a certain meta entity that can, in some cases, summarize several defined entities in
the domain Ds or partially correlate with other entities at the level of properties; however, it is possible to match the
research entity from the domain as much as possible in terms of the degree of correspondence O . This corresponds to
the main purpose of its formation according to the semantic method of comparison.
An entity is not a simple generalization or formal naming of a subset of properties. An entity is a generalization
of knowledge and informativeness over a subset of properties, considering the relationships between entities. The same
ratio connects the concepts of essence and information scope. At the same time, at the generalization level, entities can
broadcast the generalization of knowledge and informativeness to the information scope through the generalization of
properties. This relationship structure is depicted in Figure 2.
Thus, an information scope is used for comparison, as a generalization of knowledge at the level of properties
Ds Oscin ent . The set of properties is summarized by an information scope in such a way as to correspond to a certain
entity from the ontology of trust as much as possible. This method isolates knowledge to determine the degree of
alignment.
This allows for a flexible alignment method. The need for the presented model of establishing the degree of
correspondence is justified by the fact that the named entities on the set of the structured domain do not always and do
not fully correspond to the entities from the ontology according to meaningful criteria. Accordingly, to ensure a
qualitative comparison and considering the semantic heterogeneity of corpus documents, a meta-entity is formed on the
side of the structured domain, the main purpose of which is to maximally correspond to the entity on the ontology side
in terms of content.
Thus, the method is also justified by the fact that the structured domain is less formalized than the ontology and
has greater heterogeneity of both language and knowledge representation. This gives more opportunities for searching
. The set of properties is summarized by an information scope in such a way as to cor-
respond to a certain entity from the ontology of trust as much as possible. This method isolates knowledge to
determine the degree of alignment.
This allows for a flexible alignment method. The need for the presented model of establishing the degree of
correspondence is justified by the fact that the named entities on the set of the structured domain do not always and
do not fully correspond to the entities from the ontology according to meaningful criteria. Accordingly, to ensure a
qualitative comparison and considering the semantic heterogeneity of corpus documents, a meta-entity is formed on
the side of the structured domain, the main purpose of which is to maximally correspond to the entity on the ontology
side in terms of content.
360
Інформаційні системи
Thus, the method is also justified by the fact that the structured domain is less formalized than the ontol-
ogy and has greater heterogeneity of both language and knowledge representation. This gives more opportunities
for searching and forming generalizations for correspondences, and if necessary and insufficient informativeness
of the structured domain, the original source of information from the corpus of documents is obtained. The deci-
sion to establish compliance is formed using the descriptions of the corpus documents. The description of the
relevant property is taken from a certain set of documents to ensure objectivity and diversity of views.
The main variants of the form of presentation of the informative frame, shown in Figure 2, can be used in
combination. Option 9 is not implemented because the descriptor is not a complete representation of the content. The
totality of the set of descriptors
Інформаційні системи
and forming generalizations for correspondences, and if necessary and insufficient informativeness of the structured
domain, the original source of information from the corpus of documents is obtained. The decision to establish
compliance is formed using the descriptions of the corpus documents. The description of the relevant property is taken
from a certain set of documents to ensure objectivity and diversity of views.
Figure 2 – The main variants of the presentation form of the information scope
The main variants of the form of presentation of the informative frame, shown in Figure 2, can be used in
combination. Option 9 is not implemented because the descriptor is not a complete representation of the content. The
totality of the set of descriptors 1
ndcs that form property 1
nprop dcs= is represented by option 8. This is the
minimal form of formation of an informative framework with the definition of content min 1: nscin scin prop dcs= = .
Other options allow expansion when an informative frame is formed by expanding the basic formation.
Thus, we define correspondence for semantic alignment as follows
( )
( )( ) ( )( )
( )
,
, Ds Ds semanticPropertie O Ds ent Oprop Prop Fun prop prop threshhold
ent O
a
Prop
entsem ntic ent scin
Prop
= (6)
The semantic alignment function is defined using the semantic alignment of properties and is a function of the
set of descriptions dsc that are obtained from the ontology domain dom O and the structured domain of the corpus
dom Ds , respectively.
that form property
Інформаційні системи
and forming generalizations for correspondences, and if necessary and insufficient informativeness of the structured
domain, the original source of information from the corpus of documents is obtained. The decision to establish
compliance is formed using the descriptions of the corpus documents. The description of the relevant property is taken
from a certain set of documents to ensure objectivity and diversity of views.
Figure 2 – The main variants of the presentation form of the information scope
The main variants of the form of presentation of the informative frame, shown in Figure 2, can be used in
combination. Option 9 is not implemented because the descriptor is not a complete representation of the content. The
totality of the set of descriptors 1
ndcs that form property 1
nprop dcs= is represented by option 8. This is the
minimal form of formation of an informative framework with the definition of content min 1: nscin scin prop dcs= = .
Other options allow expansion when an informative frame is formed by expanding the basic formation.
Thus, we define correspondence for semantic alignment as follows
( )
( )( ) ( )( )
( )
,
, Ds Ds semanticPropertie O Ds ent Oprop Prop Fun prop prop threshhold
ent O
a
Prop
entsem ntic ent scin
Prop
= (6)
The semantic alignment function is defined using the semantic alignment of properties and is a function of the
set of descriptions dsc that are obtained from the ontology domain dom O and the structured domain of the corpus
dom Ds , respectively.
is represented by option 8. This is the minimal
form of formation of an informative framework with the definition of content
Інформаційні системи
and forming generalizations for correspondences, and if necessary and insufficient informativeness of the structured
domain, the original source of information from the corpus of documents is obtained. The decision to establish
compliance is formed using the descriptions of the corpus documents. The description of the relevant property is taken
from a certain set of documents to ensure objectivity and diversity of views.
Figure 2 – The main variants of the presentation form of the information scope
The main variants of the form of presentation of the informative frame, shown in Figure 2, can be used in
combination. Option 9 is not implemented because the descriptor is not a complete representation of the content. The
totality of the set of descriptors 1
ndcs that form property 1
nprop dcs= is represented by option 8. This is the
minimal form of formation of an informative framework with the definition of content min 1: nscin scin prop dcs= = .
Other options allow expansion when an informative frame is formed by expanding the basic formation.
Thus, we define correspondence for semantic alignment as follows
( )
( )( ) ( )( )
( )
,
, Ds Ds semanticPropertie O Ds ent Oprop Prop Fun prop prop threshhold
ent O
a
Prop
entsem ntic ent scin
Prop
= (6)
The semantic alignment function is defined using the semantic alignment of properties and is a function of the
set of descriptions dsc that are obtained from the ontology domain dom O and the structured domain of the corpus
dom Ds , respectively.
. Other op-
tions allow expansion when an informative frame is formed by expanding the basic formation.
Thus, we define correspondence for semantic alignment as follows
Інформаційні системи
and forming generalizations for correspondences, and if necessary and insufficient informativeness of the structured
domain, the original source of information from the corpus of documents is obtained. The decision to establish
compliance is formed using the descriptions of the corpus documents. The description of the relevant property is taken
from a certain set of documents to ensure objectivity and diversity of views.
Figure 2 – The main variants of the presentation form of the information scope
The main variants of the form of presentation of the informative frame, shown in Figure 2, can be used in
combination. Option 9 is not implemented because the descriptor is not a complete representation of the content. The
totality of the set of descriptors 1
ndcs that form property 1
nprop dcs= is represented by option 8. This is the
minimal form of formation of an informative framework with the definition of content min 1: nscin scin prop dcs= = .
Other options allow expansion when an informative frame is formed by expanding the basic formation.
Thus, we define correspondence for semantic alignment as follows
( )
( )( ) ( )( )
( )
,
, Ds Ds semanticPropertie O Ds ent Oprop Prop Fun prop prop threshhold
ent O
a
Prop
entsem ntic ent scin
Prop
= (6)
The semantic alignment function is defined using the semantic alignment of properties and is a function of the
set of descriptions dsc that are obtained from the ontology domain dom O and the structured domain of the corpus
dom Ds , respectively.
(6)
The semantic alignment function is defined using the semantic alignment of properties and is a function of
the set of descriptions
Інформаційні системи
and forming generalizations for correspondences, and if necessary and insufficient informativeness of the structured
domain, the original source of information from the corpus of documents is obtained. The decision to establish
compliance is formed using the descriptions of the corpus documents. The description of the relevant property is taken
from a certain set of documents to ensure objectivity and diversity of views.
Figure 2 – The main variants of the presentation form of the information scope
The main variants of the form of presentation of the informative frame, shown in Figure 2, can be used in
combination. Option 9 is not implemented because the descriptor is not a complete representation of the content. The
totality of the set of descriptors 1
ndcs that form property 1
nprop dcs= is represented by option 8. This is the
minimal form of formation of an informative framework with the definition of content min 1: nscin scin prop dcs= = .
Other options allow expansion when an informative frame is formed by expanding the basic formation.
Thus, we define correspondence for semantic alignment as follows
( )
( )( ) ( )( )
( )
,
, Ds Ds semanticPropertie O Ds ent Oprop Prop Fun prop prop threshhold
ent O
a
Prop
entsem ntic ent scin
Prop
= (6)
The semantic alignment function is defined using the semantic alignment of properties and is a function of the
set of descriptions dsc that are obtained from the ontology domain dom O and the structured domain of the corpus
dom Ds , respectively.
that are obtained from the ontology domain
Інформаційні системи
and forming generalizations for correspondences, and if necessary and insufficient informativeness of the structured
domain, the original source of information from the corpus of documents is obtained. The decision to establish
compliance is formed using the descriptions of the corpus documents. The description of the relevant property is taken
from a certain set of documents to ensure objectivity and diversity of views.
Figure 2 – The main variants of the presentation form of the information scope
The main variants of the form of presentation of the informative frame, shown in Figure 2, can be used in
combination. Option 9 is not implemented because the descriptor is not a complete representation of the content. The
totality of the set of descriptors 1
ndcs that form property 1
nprop dcs= is represented by option 8. This is the
minimal form of formation of an informative framework with the definition of content min 1: nscin scin prop dcs= = .
Other options allow expansion when an informative frame is formed by expanding the basic formation.
Thus, we define correspondence for semantic alignment as follows
( )
( )( ) ( )( )
( )
,
, Ds Ds semanticPropertie O Ds ent Oprop Prop Fun prop prop threshhold
ent O
a
Prop
entsem ntic ent scin
Prop
= (6)
The semantic alignment function is defined using the semantic alignment of properties and is a function of the
set of descriptions dsc that are obtained from the ontology domain dom O and the structured domain of the corpus
dom Ds , respectively.
and the structured domain of the corpus
Інформаційні системи
and forming generalizations for correspondences, and if necessary and insufficient informativeness of the structured
domain, the original source of information from the corpus of documents is obtained. The decision to establish
compliance is formed using the descriptions of the corpus documents. The description of the relevant property is taken
from a certain set of documents to ensure objectivity and diversity of views.
Figure 2 – The main variants of the presentation form of the information scope
The main variants of the form of presentation of the informative frame, shown in Figure 2, can be used in
combination. Option 9 is not implemented because the descriptor is not a complete representation of the content. The
totality of the set of descriptors 1
ndcs that form property 1
nprop dcs= is represented by option 8. This is the
minimal form of formation of an informative framework with the definition of content min 1: nscin scin prop dcs= = .
Other options allow expansion when an informative frame is formed by expanding the basic formation.
Thus, we define correspondence for semantic alignment as follows
( )
( )( ) ( )( )
( )
,
, Ds Ds semanticPropertie O Ds ent Oprop Prop Fun prop prop threshhold
ent O
a
Prop
entsem ntic ent scin
Prop
= (6)
The semantic alignment function is defined using the semantic alignment of properties and is a function of the
set of descriptions dsc that are obtained from the ontology domain dom O and the structured domain of the corpus
dom Ds , respectively. , respectively.
Інформаційні системи
[Введите текст]
( ) ( ),O Ds O Dsprops , propemanticPropert f dsc di sce = (7)
The number of found alignment properties on the set of ontology properties relative to the essence of the
research ( )ent OProp and limited by the selection function from those properties that have a match on the set Ds , i.e.
determined ( )O DssemanticPropert e prop , ropi p whose value is greater than the threshold, is estimated. The assessment
is determined by the total number of the study essence properties ent . To establish a unified designation of the entities
of the syntactic, structural, and semantic levels, a certain entity ent from Ds . This entity can be considered as a first
approximation of the corresponding entity from Ds . In the future, the entity can move into an information scope
ent scin , the set of properties of which corresponds to a greater extent to the set of properties of the research entity
by the heterogeneity of language and knowledge representation.
Experimental studies
Experimental studies were conducted to determine the effectiveness of the method of semantic determination of
the alignment of ontologies content categories with the generalization of descriptive structures. Alignment is performed
for an ontology [24] and a certain structured corpus [17].
Figure 3 – Distribution of AI trust ontology concepts within the corpus
Analysis using semantic comparison of ontologies and a structured domain allows for determining the relevance
of the importance of AI reliability components. There are variations in the names of concepts; that is, concepts with a
similar structure can have different lexical names. However, when using semantic comparison, the names of the
concepts do not play an important role. In the conceptual categories {Transparency} and {Privacy}, their prevalence
and importance is determined by the shares of 88.24% and 59.52%, respectively. According to the content complexity,
the content of {Explainability} is embedded in {Transparency}. Further, the comparison using lexical variability using
the content definition {strategies for reduction Bias} corresponds to the concept {Justice & fairness}, and {functional
Safety} corresponds to the concept {Non-maleficence} and has 75% and 72.62% importance. According to the smaller
corresponding fraction, the partial semantic match {Controllability} has a lexical counterpart {Freedom & autonomy}
with a prevalence of 44.05%. {Trust} concept with a spread of 36.9%. Concepts {Responsibility} are presented in the
composition of 69.05%, {Beneficence} respectively 51.19%. Concepts {Sustainability}, {Dignity}, {Solidarity} are
presented at the level of group closeness. The purpose of the comparison is to include concepts and generalizations as
much as possible. The comparison demonstrates the effectiveness of the semantic comparison of ontology and
structured domains.
Conclusions and discussion
The developed method of alignment of the essences of ontologies or structured domains allows formalizing the
alignment process. It provides significant diversity in the form of representation of informativeness. Such cases arise
due to the non-strict correspondence of the essences of the alignment, significant inconsistency, and descriptive
complexity. The method allows determining the basic content unit as a descriptor. This allowed the field of knowledge
to be presented in a discrete form with the form of an atomic representation of content. Further hierarchical
88,24%
75,00% 72,62% 69,05%
59,52%
51,19%
44,05%
36,90%
20,24% 17,86%
9,52%
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
(7)
The number of found alignment properties on the set of ontology properties relative to the essence of the re-
search
Інформаційні системи
[Введите текст]
( ) ( ),O Ds O Dsprops , propemanticPropert f dsc di sce = (7)
The number of found alignment properties on the set of ontology properties relative to the essence of the
research ( )ent OProp and limited by the selection function from those properties that have a match on the set Ds , i.e.
determined ( )O DssemanticPropert e prop , ropi p whose value is greater than the threshold, is estimated. The assessment
is determined by the total number of the study essence properties ent . To establish a unified designation of the entities
of the syntactic, structural, and semantic levels, a certain entity ent from Ds . This entity can be considered as a first
approximation of the corresponding entity from Ds . In the future, the entity can move into an information scope
ent scin , the set of properties of which corresponds to a greater extent to the set of properties of the research entity
by the heterogeneity of language and knowledge representation.
Experimental studies
Experimental studies were conducted to determine the effectiveness of the method of semantic determination of
the alignment of ontologies content categories with the generalization of descriptive structures. Alignment is performed
for an ontology [24] and a certain structured corpus [17].
Figure 3 – Distribution of AI trust ontology concepts within the corpus
Analysis using semantic comparison of ontologies and a structured domain allows for determining the relevance
of the importance of AI reliability components. There are variations in the names of concepts; that is, concepts with a
similar structure can have different lexical names. However, when using semantic comparison, the names of the
concepts do not play an important role. In the conceptual categories {Transparency} and {Privacy}, their prevalence
and importance is determined by the shares of 88.24% and 59.52%, respectively. According to the content complexity,
the content of {Explainability} is embedded in {Transparency}. Further, the comparison using lexical variability using
the content definition {strategies for reduction Bias} corresponds to the concept {Justice & fairness}, and {functional
Safety} corresponds to the concept {Non-maleficence} and has 75% and 72.62% importance. According to the smaller
corresponding fraction, the partial semantic match {Controllability} has a lexical counterpart {Freedom & autonomy}
with a prevalence of 44.05%. {Trust} concept with a spread of 36.9%. Concepts {Responsibility} are presented in the
composition of 69.05%, {Beneficence} respectively 51.19%. Concepts {Sustainability}, {Dignity}, {Solidarity} are
presented at the level of group closeness. The purpose of the comparison is to include concepts and generalizations as
much as possible. The comparison demonstrates the effectiveness of the semantic comparison of ontology and
structured domains.
Conclusions and discussion
The developed method of alignment of the essences of ontologies or structured domains allows formalizing the
alignment process. It provides significant diversity in the form of representation of informativeness. Such cases arise
due to the non-strict correspondence of the essences of the alignment, significant inconsistency, and descriptive
complexity. The method allows determining the basic content unit as a descriptor. This allowed the field of knowledge
to be presented in a discrete form with the form of an atomic representation of content. Further hierarchical
88,24%
75,00% 72,62% 69,05%
59,52%
51,19%
44,05%
36,90%
20,24% 17,86%
9,52%
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
and limited by the selection function from those properties that have a match on the set
Інформаційні системи
[Введите текст]
( ) ( ),O Ds O Dsprops , propemanticPropert f dsc di sce = (7)
The number of found alignment properties on the set of ontology properties relative to the essence of the
research ( )ent OProp and limited by the selection function from those properties that have a match on the set Ds , i.e.
determined ( )O DssemanticPropert e prop , ropi p whose value is greater than the threshold, is estimated. The assessment
is determined by the total number of the study essence properties ent . To establish a unified designation of the entities
of the syntactic, structural, and semantic levels, a certain entity ent from Ds . This entity can be considered as a first
approximation of the corresponding entity from Ds . In the future, the entity can move into an information scope
ent scin , the set of properties of which corresponds to a greater extent to the set of properties of the research entity
by the heterogeneity of language and knowledge representation.
Experimental studies
Experimental studies were conducted to determine the effectiveness of the method of semantic determination of
the alignment of ontologies content categories with the generalization of descriptive structures. Alignment is performed
for an ontology [24] and a certain structured corpus [17].
Figure 3 – Distribution of AI trust ontology concepts within the corpus
Analysis using semantic comparison of ontologies and a structured domain allows for determining the relevance
of the importance of AI reliability components. There are variations in the names of concepts; that is, concepts with a
similar structure can have different lexical names. However, when using semantic comparison, the names of the
concepts do not play an important role. In the conceptual categories {Transparency} and {Privacy}, their prevalence
and importance is determined by the shares of 88.24% and 59.52%, respectively. According to the content complexity,
the content of {Explainability} is embedded in {Transparency}. Further, the comparison using lexical variability using
the content definition {strategies for reduction Bias} corresponds to the concept {Justice & fairness}, and {functional
Safety} corresponds to the concept {Non-maleficence} and has 75% and 72.62% importance. According to the smaller
corresponding fraction, the partial semantic match {Controllability} has a lexical counterpart {Freedom & autonomy}
with a prevalence of 44.05%. {Trust} concept with a spread of 36.9%. Concepts {Responsibility} are presented in the
composition of 69.05%, {Beneficence} respectively 51.19%. Concepts {Sustainability}, {Dignity}, {Solidarity} are
presented at the level of group closeness. The purpose of the comparison is to include concepts and generalizations as
much as possible. The comparison demonstrates the effectiveness of the semantic comparison of ontology and
structured domains.
Conclusions and discussion
The developed method of alignment of the essences of ontologies or structured domains allows formalizing the
alignment process. It provides significant diversity in the form of representation of informativeness. Such cases arise
due to the non-strict correspondence of the essences of the alignment, significant inconsistency, and descriptive
complexity. The method allows determining the basic content unit as a descriptor. This allowed the field of knowledge
to be presented in a discrete form with the form of an atomic representation of content. Further hierarchical
88,24%
75,00% 72,62% 69,05%
59,52%
51,19%
44,05%
36,90%
20,24% 17,86%
9,52%
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
, i.e.
determined
Інформаційні системи
[Введите текст]
( ) ( ),O Ds O Dsprops , propemanticPropert f dsc di sce = (7)
The number of found alignment properties on the set of ontology properties relative to the essence of the
research ( )ent OProp and limited by the selection function from those properties that have a match on the set Ds , i.e.
determined ( )O DssemanticPropert e prop , ropi p whose value is greater than the threshold, is estimated. The assessment
is determined by the total number of the study essence properties ent . To establish a unified designation of the entities
of the syntactic, structural, and semantic levels, a certain entity ent from Ds . This entity can be considered as a first
approximation of the corresponding entity from Ds . In the future, the entity can move into an information scope
ent scin , the set of properties of which corresponds to a greater extent to the set of properties of the research entity
by the heterogeneity of language and knowledge representation.
Experimental studies
Experimental studies were conducted to determine the effectiveness of the method of semantic determination of
the alignment of ontologies content categories with the generalization of descriptive structures. Alignment is performed
for an ontology [24] and a certain structured corpus [17].
Figure 3 – Distribution of AI trust ontology concepts within the corpus
Analysis using semantic comparison of ontologies and a structured domain allows for determining the relevance
of the importance of AI reliability components. There are variations in the names of concepts; that is, concepts with a
similar structure can have different lexical names. However, when using semantic comparison, the names of the
concepts do not play an important role. In the conceptual categories {Transparency} and {Privacy}, their prevalence
and importance is determined by the shares of 88.24% and 59.52%, respectively. According to the content complexity,
the content of {Explainability} is embedded in {Transparency}. Further, the comparison using lexical variability using
the content definition {strategies for reduction Bias} corresponds to the concept {Justice & fairness}, and {functional
Safety} corresponds to the concept {Non-maleficence} and has 75% and 72.62% importance. According to the smaller
corresponding fraction, the partial semantic match {Controllability} has a lexical counterpart {Freedom & autonomy}
with a prevalence of 44.05%. {Trust} concept with a spread of 36.9%. Concepts {Responsibility} are presented in the
composition of 69.05%, {Beneficence} respectively 51.19%. Concepts {Sustainability}, {Dignity}, {Solidarity} are
presented at the level of group closeness. The purpose of the comparison is to include concepts and generalizations as
much as possible. The comparison demonstrates the effectiveness of the semantic comparison of ontology and
structured domains.
Conclusions and discussion
The developed method of alignment of the essences of ontologies or structured domains allows formalizing the
alignment process. It provides significant diversity in the form of representation of informativeness. Such cases arise
due to the non-strict correspondence of the essences of the alignment, significant inconsistency, and descriptive
complexity. The method allows determining the basic content unit as a descriptor. This allowed the field of knowledge
to be presented in a discrete form with the form of an atomic representation of content. Further hierarchical
88,24%
75,00% 72,62% 69,05%
59,52%
51,19%
44,05%
36,90%
20,24% 17,86%
9,52%
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
whose value is greater than the threshold, is estimated. The assessment
is determined by the total number of the study essence properties
Інформаційні системи
[Введите текст]
( ) ( ),O Ds O Dsprops , propemanticPropert f dsc di sce = (7)
The number of found alignment properties on the set of ontology properties relative to the essence of the
research ( )ent OProp and limited by the selection function from those properties that have a match on the set Ds , i.e.
determined ( )O DssemanticPropert e prop , ropi p whose value is greater than the threshold, is estimated. The assessment
is determined by the total number of the study essence properties ent . To establish a unified designation of the entities
of the syntactic, structural, and semantic levels, a certain entity ent from Ds . This entity can be considered as a first
approximation of the corresponding entity from Ds . In the future, the entity can move into an information scope
ent scin , the set of properties of which corresponds to a greater extent to the set of properties of the research entity
by the heterogeneity of language and knowledge representation.
Experimental studies
Experimental studies were conducted to determine the effectiveness of the method of semantic determination of
the alignment of ontologies content categories with the generalization of descriptive structures. Alignment is performed
for an ontology [24] and a certain structured corpus [17].
Figure 3 – Distribution of AI trust ontology concepts within the corpus
Analysis using semantic comparison of ontologies and a structured domain allows for determining the relevance
of the importance of AI reliability components. There are variations in the names of concepts; that is, concepts with a
similar structure can have different lexical names. However, when using semantic comparison, the names of the
concepts do not play an important role. In the conceptual categories {Transparency} and {Privacy}, their prevalence
and importance is determined by the shares of 88.24% and 59.52%, respectively. According to the content complexity,
the content of {Explainability} is embedded in {Transparency}. Further, the comparison using lexical variability using
the content definition {strategies for reduction Bias} corresponds to the concept {Justice & fairness}, and {functional
Safety} corresponds to the concept {Non-maleficence} and has 75% and 72.62% importance. According to the smaller
corresponding fraction, the partial semantic match {Controllability} has a lexical counterpart {Freedom & autonomy}
with a prevalence of 44.05%. {Trust} concept with a spread of 36.9%. Concepts {Responsibility} are presented in the
composition of 69.05%, {Beneficence} respectively 51.19%. Concepts {Sustainability}, {Dignity}, {Solidarity} are
presented at the level of group closeness. The purpose of the comparison is to include concepts and generalizations as
much as possible. The comparison demonstrates the effectiveness of the semantic comparison of ontology and
structured domains.
Conclusions and discussion
The developed method of alignment of the essences of ontologies or structured domains allows formalizing the
alignment process. It provides significant diversity in the form of representation of informativeness. Such cases arise
due to the non-strict correspondence of the essences of the alignment, significant inconsistency, and descriptive
complexity. The method allows determining the basic content unit as a descriptor. This allowed the field of knowledge
to be presented in a discrete form with the form of an atomic representation of content. Further hierarchical
88,24%
75,00% 72,62% 69,05%
59,52%
51,19%
44,05%
36,90%
20,24% 17,86%
9,52%
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
. To establish a unified designation of the enti-
ties of the syntactic, structural, and semantic levels, a certain entity
Інформаційні системи
[Введите текст]
( ) ( ),O Ds O Dsprops , propemanticPropert f dsc di sce = (7)
The number of found alignment properties on the set of ontology properties relative to the essence of the
research ( )ent OProp and limited by the selection function from those properties that have a match on the set Ds , i.e.
determined ( )O DssemanticPropert e prop , ropi p whose value is greater than the threshold, is estimated. The assessment
is determined by the total number of the study essence properties ent . To establish a unified designation of the entities
of the syntactic, structural, and semantic levels, a certain entity ent from Ds . This entity can be considered as a first
approximation of the corresponding entity from Ds . In the future, the entity can move into an information scope
ent scin , the set of properties of which corresponds to a greater extent to the set of properties of the research entity
by the heterogeneity of language and knowledge representation.
Experimental studies
Experimental studies were conducted to determine the effectiveness of the method of semantic determination of
the alignment of ontologies content categories with the generalization of descriptive structures. Alignment is performed
for an ontology [24] and a certain structured corpus [17].
Figure 3 – Distribution of AI trust ontology concepts within the corpus
Analysis using semantic comparison of ontologies and a structured domain allows for determining the relevance
of the importance of AI reliability components. There are variations in the names of concepts; that is, concepts with a
similar structure can have different lexical names. However, when using semantic comparison, the names of the
concepts do not play an important role. In the conceptual categories {Transparency} and {Privacy}, their prevalence
and importance is determined by the shares of 88.24% and 59.52%, respectively. According to the content complexity,
the content of {Explainability} is embedded in {Transparency}. Further, the comparison using lexical variability using
the content definition {strategies for reduction Bias} corresponds to the concept {Justice & fairness}, and {functional
Safety} corresponds to the concept {Non-maleficence} and has 75% and 72.62% importance. According to the smaller
corresponding fraction, the partial semantic match {Controllability} has a lexical counterpart {Freedom & autonomy}
with a prevalence of 44.05%. {Trust} concept with a spread of 36.9%. Concepts {Responsibility} are presented in the
composition of 69.05%, {Beneficence} respectively 51.19%. Concepts {Sustainability}, {Dignity}, {Solidarity} are
presented at the level of group closeness. The purpose of the comparison is to include concepts and generalizations as
much as possible. The comparison demonstrates the effectiveness of the semantic comparison of ontology and
structured domains.
Conclusions and discussion
The developed method of alignment of the essences of ontologies or structured domains allows formalizing the
alignment process. It provides significant diversity in the form of representation of informativeness. Such cases arise
due to the non-strict correspondence of the essences of the alignment, significant inconsistency, and descriptive
complexity. The method allows determining the basic content unit as a descriptor. This allowed the field of knowledge
to be presented in a discrete form with the form of an atomic representation of content. Further hierarchical
88,24%
75,00% 72,62% 69,05%
59,52%
51,19%
44,05%
36,90%
20,24% 17,86%
9,52%
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
from Ds. This entity can be considered as a
first approximation of the corresponding entity from Ds. In the future, the entity can move into an information scope
Інформаційні системи
[Введите текст]
( ) ( ),O Ds O Dsprops , propemanticPropert f dsc di sce = (7)
The number of found alignment properties on the set of ontology properties relative to the essence of the
research ( )ent OProp and limited by the selection function from those properties that have a match on the set Ds , i.e.
determined ( )O DssemanticPropert e prop , ropi p whose value is greater than the threshold, is estimated. The assessment
is determined by the total number of the study essence properties ent . To establish a unified designation of the entities
of the syntactic, structural, and semantic levels, a certain entity ent from Ds . This entity can be considered as a first
approximation of the corresponding entity from Ds . In the future, the entity can move into an information scope
ent scin , the set of properties of which corresponds to a greater extent to the set of properties of the research entity
by the heterogeneity of language and knowledge representation.
Experimental studies
Experimental studies were conducted to determine the effectiveness of the method of semantic determination of
the alignment of ontologies content categories with the generalization of descriptive structures. Alignment is performed
for an ontology [24] and a certain structured corpus [17].
Figure 3 – Distribution of AI trust ontology concepts within the corpus
Analysis using semantic comparison of ontologies and a structured domain allows for determining the relevance
of the importance of AI reliability components. There are variations in the names of concepts; that is, concepts with a
similar structure can have different lexical names. However, when using semantic comparison, the names of the
concepts do not play an important role. In the conceptual categories {Transparency} and {Privacy}, their prevalence
and importance is determined by the shares of 88.24% and 59.52%, respectively. According to the content complexity,
the content of {Explainability} is embedded in {Transparency}. Further, the comparison using lexical variability using
the content definition {strategies for reduction Bias} corresponds to the concept {Justice & fairness}, and {functional
Safety} corresponds to the concept {Non-maleficence} and has 75% and 72.62% importance. According to the smaller
corresponding fraction, the partial semantic match {Controllability} has a lexical counterpart {Freedom & autonomy}
with a prevalence of 44.05%. {Trust} concept with a spread of 36.9%. Concepts {Responsibility} are presented in the
composition of 69.05%, {Beneficence} respectively 51.19%. Concepts {Sustainability}, {Dignity}, {Solidarity} are
presented at the level of group closeness. The purpose of the comparison is to include concepts and generalizations as
much as possible. The comparison demonstrates the effectiveness of the semantic comparison of ontology and
structured domains.
Conclusions and discussion
The developed method of alignment of the essences of ontologies or structured domains allows formalizing the
alignment process. It provides significant diversity in the form of representation of informativeness. Such cases arise
due to the non-strict correspondence of the essences of the alignment, significant inconsistency, and descriptive
complexity. The method allows determining the basic content unit as a descriptor. This allowed the field of knowledge
to be presented in a discrete form with the form of an atomic representation of content. Further hierarchical
88,24%
75,00% 72,62% 69,05%
59,52%
51,19%
44,05%
36,90%
20,24% 17,86%
9,52%
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
, the set of properties of which corresponds to a greater extent to the set of properties of the research entity
by the heterogeneity of language and knowledge representation.
Experimental studies
Experimental studies were conducted to determine the effectiveness of the method of semantic determination of
the alignment of ontologies content categories with the generalization of descriptive structures. Alignment is performed
for an ontology [24] and a certain structured corpus [17].
Інформаційні системи
[Введите текст]
( ) ( ),O Ds O Dsprops , propemanticPropert f dsc di sce = (7)
The number of found alignment properties on the set of ontology properties relative to the essence of the
research ( )ent OProp and limited by the selection function from those properties that have a match on the set Ds , i.e.
determined ( )O DssemanticPropert e prop , ropi p whose value is greater than the threshold, is estimated. The assessment
is determined by the total number of the study essence properties ent . To establish a unified designation of the entities
of the syntactic, structural, and semantic levels, a certain entity ent from Ds . This entity can be considered as a first
approximation of the corresponding entity from Ds . In the future, the entity can move into an information scope
ent scin , the set of properties of which corresponds to a greater extent to the set of properties of the research entity
by the heterogeneity of language and knowledge representation.
Experimental studies
Experimental studies were conducted to determine the effectiveness of the method of semantic determination of
the alignment of ontologies content categories with the generalization of descriptive structures. Alignment is performed
for an ontology [24] and a certain structured corpus [17].
Figure 3 – Distribution of AI trust ontology concepts within the corpus
Analysis using semantic comparison of ontologies and a structured domain allows for determining the relevance
of the importance of AI reliability components. There are variations in the names of concepts; that is, concepts with a
similar structure can have different lexical names. However, when using semantic comparison, the names of the
concepts do not play an important role. In the conceptual categories {Transparency} and {Privacy}, their prevalence
and importance is determined by the shares of 88.24% and 59.52%, respectively. According to the content complexity,
the content of {Explainability} is embedded in {Transparency}. Further, the comparison using lexical variability using
the content definition {strategies for reduction Bias} corresponds to the concept {Justice & fairness}, and {functional
Safety} corresponds to the concept {Non-maleficence} and has 75% and 72.62% importance. According to the smaller
corresponding fraction, the partial semantic match {Controllability} has a lexical counterpart {Freedom & autonomy}
with a prevalence of 44.05%. {Trust} concept with a spread of 36.9%. Concepts {Responsibility} are presented in the
composition of 69.05%, {Beneficence} respectively 51.19%. Concepts {Sustainability}, {Dignity}, {Solidarity} are
presented at the level of group closeness. The purpose of the comparison is to include concepts and generalizations as
much as possible. The comparison demonstrates the effectiveness of the semantic comparison of ontology and
structured domains.
Conclusions and discussion
The developed method of alignment of the essences of ontologies or structured domains allows formalizing the
alignment process. It provides significant diversity in the form of representation of informativeness. Such cases arise
due to the non-strict correspondence of the essences of the alignment, significant inconsistency, and descriptive
complexity. The method allows determining the basic content unit as a descriptor. This allowed the field of knowledge
to be presented in a discrete form with the form of an atomic representation of content. Further hierarchical
88,24%
75,00% 72,62% 69,05%
59,52%
51,19%
44,05%
36,90%
20,24% 17,86%
9,52%
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
Figure 3. Distribution of AI trust ontology concepts within the corpus
Analysis using semantic comparison of ontologies and a structured domain allows for determining the
relevance of the importance of AI reliability components. There are variations in the names of concepts; that is,
concepts with a similar structure can have different lexical names. However, when using semantic comparison, the
names of the concepts do not play an important role. In the conceptual categories {Transparency} and {Privacy},
361
Інформаційні системи
their prevalence and importance is determined by the shares of 88.24% and 59.52%, respectively. According to the
content complexity, the content of {Explainability} is embedded in {Transparency}. Further, the comparison using
lexical variability using the content definition {strategies for reduction Bias} corresponds to the concept {Justice &
fairness}, and {functional Safety} corresponds to the concept {Non-maleficence} and has 75% and 72.62% impor-
tance. According to the smaller corresponding fraction, the partial semantic match {Controllability} has a lexical
counterpart {Freedom & autonomy} with a prevalence of 44.05%. {Trust} concept with a spread of 36.9%. Con-
cepts {Responsibility} are presented in the composition of 69.05%, {Beneficence} respectively 51.19%. Concepts
{Sustainability}, {Dignity}, {Solidarity} are presented at the level of group closeness. The purpose of the compari-
son is to include concepts and generalizations as much as possible. The comparison demonstrates the effectiveness
of the semantic comparison of ontology and structured domains.
Conclusions and discussion
The developed method of alignment of the essences of ontologies or structured domains allows formalizing
the alignment process. It provides significant diversity in the form of representation of informativeness. Such cases
arise due to the non-strict correspondence of the essences of the alignment, significant inconsistency, and descriptive
complexity. The method allows determining the basic content unit as a descriptor. This allowed the field of knowledge
to be presented in a discrete form with the form of an atomic representation of content. Further hierarchical generaliza-
tion makes it possible to form properties that form an informative frame as entities of maximum correspondence to the
object of alignment. The method provides the discretization of the knowledge domain and the necessary flexibility for
a formal approach. The form of presentation of knowledge in the area of matching with the application of the proposed
method does not play a significant role. This is because the entities of correspondence are formed and built from a
discrete field of atomic representation of meaningfulness. Moreover, meaningfulness is formed in separate knowledge
and not in the form of dispersed descriptiveness of unique content. The application of the method of semantic determi-
nation of the correspondence of meaningful categories of ontologies with the generalization of descriptive structures
allows for formalizing the alignment process and improving the quality of semantic inference.
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Received 03.08.2022
Про авторів:
1Манзюк Едуард Андрійович,
доцент кафедри комп’ютерних наук;
86 друк. праць, в т.ч.: 48 наук. статей у фахових вид.,
25 зарубіжних публікацій
Індекс Хірша - 5
ORCID iD: 0000-0002-7310-2126
2Бармак Олександр Володимирович,
доктор технічних наук, професор,
завідувач кафедри комп’ютерних наук ;
понад 100 друк. праць, в т.ч.: 45 наук. статей у фахових вид.,
12 зарубіжних публікацій
Індекс Хірша - 10
ORCID iD: 0000-0003-0739-9678
363
Інформаційні системи
3Крак Юрій Васильович,
доктор фізико-математичних наук, професор,
завідувач кафедри теоретичної кібернетики;
понад 700 друк. праць, в т.ч.: 177 наук. статей у фахових вид.,
93 зарубіжних публікацій;
Індекс Хірша - 14
ORCID iD: 0000-0002-8043-0785
4Пасічник Олександр Анатолійович,
доцент кафедри комп’ютерних наук ;
122 друк. праць, в т.ч.: 49 наук. статей у фахових вид.,
1 зарубіжних публікацій
Індекс Хірша - 1
ORCID iD: 0000-0002-8760-4688
5Радюк Павло Михайлович,
PhD; старший викладач кафедри комп’ютерних наук
20 друк. праць, в т.ч.: 16 наук. статей у фахових вид.,
10 зарубіжних публікацій
Індекс Хірша - 2
ORCID iD: 0000-0003-3609-112X
6Мазурець Олександр Вікторович
кандидат технічних наук, доцент кафедри комп’ютерних наук
155 друк. праць, в т.ч.: 31 наук. статей у фахових вид.,
11 зарубіжних публікацій
ORCID iD: 0000-0002-8900-0650
Місце роботи авторів:
3Київський національний університет імені Тараса Шевченка,
01601, Київ, вул. Володимирська, 60;
Інститут кібернетики імені В.М.Глушкова НАН України,
03187, Київ, пр. Глушкова, 40;
E-mail: Iurii.krak@knu.ua, yuri.krak@gmail.com
1,2,4,5,6Хмельницький національний університет МОН України,
29016, Хмельницький, вул. Інститутська, 11;
E-mail: eduard.em.km@gmail.com, alexander.barmak@gmail.com,
o.a.pasichnyk@gmail.com, radiukpavlo@gmail.com, exechong@gmail.com
Прізвища та ініціали авторів і назва доповіді англійською мовою:
Manziuk E.A., Barmak O.V., Krak Iu.V., Pasichnyk O.A., Radiuk P.M., Mazurets O.V.
Semantic alignment of ontologies meaningful categories with the generalization
of descriptive structures
Прізвища та ініціали авторів і назва доповіді українською мовою:
Манзюк E.A., Бармак O.В., Крак Ю.В., Пасічник O.A., Радюк П.M., Мазурець O.В.
Метод семантичного визначення відповідності змістовних категорій онтологій
із узагальненням описових структур
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