The main statements of ontology theory and its implementation in the system of legal knowledge
The paper presents general information about a notion “ontology” historical derivation. Apart from this different ways of ontology term transformation for usage in artificial intelligence systems are analyzed. Ontology is regarded there as a complex of knowledge for clear representation of the data...
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Інститут проблем моделювання в енергетиці ім. Г.Є. Пухова НАН України
2018
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nasplib_isofts_kiev_ua-123456789-1312452025-02-10T01:21:52Z The main statements of ontology theory and its implementation in the system of legal knowledge Основные положения теории онтологий и ее внедрение в систему правовых знаний Kosenko, S.O. Применение методов и средств моделирования The paper presents general information about a notion “ontology” historical derivation. Apart from this different ways of ontology term transformation for usage in artificial intelligence systems are analyzed. Ontology is regarded there as a complex of knowledge for clear representation of the data about events, phenomena, general and special notions concerning society, laws and the world. Apart from this, ontology is developed to supply different information about the subject of interest. There are a number of ontologies, namely surface, top, domain ones and so on, which form a base for further development of knowledge based systems and their application in combination with artificial intelligence and a set of databases for improving the process of logical thinking and making relevant decisions. Ontologies are of particular importance for law and legal theory for rule formalization, accepting court resolutions and providing information about precedents and untypical cases. The ontology design criteria are also given along with the peculiarities of their application in legal domain. Ontologies are formed with specific goals, but there are no ways of forming their contents and design. The main task to be followed in ontology creation deals with the strict and clear formulation of the idea of ontology with allowance for the link between different ontologies. Подано загальні відомості про походження поняття «онтологія» та проаналізовано шляхи його подальшої трансформації для використання в системах штучного інтелекту, де онтологія розглядається як комплекс знань, що надають певну інформацію про об’єкт дослідження. На теперішній час розроблено низку різноманітних онтологій, а саме поверхневі, топові, доменні тощо, котрі є основою при розробці системи штучного інтелекту з використанням накопичених знань та баз даних для удосконалення процесу логічного мислення і прийняття відповідних рішень. Особливого значення набули онтології в правознавстві для формалізації законів, прийняття судових рішень та надання інформації про певні прецеденти та нетипові випадкі. Наведено критерії дизайну онтологій, а також особливості їх застосування в правовому домені. Формування онтологій має специфічні завдання, однак відсутні які-небудь способи формування їх змісту та дизайну. Головне завдання, якого слід дотримуватися при створенні онтології, полягає у суворому і чіткому формулюванні суті онтології, враховуючи зв’язок між різними онтологіями. Представлены обобщенные данные о происхождении понятия «онтология» и проанализированы пути его дальнейшей трансформации для применения в системах искусственного интеллекта, где онтология понимается как комплекс знаний, представляющих определенную информацию об объекте исследования. В настоящее время разработан ряд различных онтологий, в частности поверхностные, топовые, доменные и другие, которые являются основой при разработке системы искусственного интеллекта с использованием накопленных знаний и баз данных для усовершенствования процесса логического мышления и принятия соответствующих решений. Особое значение имеют онтологии в правоведении для формализации законов, принятия судебных решений и подачи информации об определенных прецедентах и нетипичных случаях. Описаны критерии дизайна онтологий, а также особенности их применения в правовом домене. Формирование онтологий имеет специфические задачи, но отсутствуют какие-либо способы формирования их содержания и дизайна. Основная задача, которая должна быть выполнена при создании онтологии, связана со строгим и четким формулированием сути понятия «онтология», учитывая связь между различными онтологиями. 2018 Article The main statements of ontology theory and its implementation in the system of legal knowledge / S.O. Kosenko // Электронное моделирование. — 2018. — Т. 40, № 1. — С. 95-113. — Бібліогр.: 42 назв. — англ. 0204-3572 https://nasplib.isofts.kiev.ua/handle/123456789/131245 004.91+004.81/34.01 en Электронное моделирование application/pdf Інститут проблем моделювання в енергетиці ім. Г.Є. Пухова НАН України |
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Применение методов и средств моделирования Применение методов и средств моделирования |
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Применение методов и средств моделирования Применение методов и средств моделирования Kosenko, S.O. The main statements of ontology theory and its implementation in the system of legal knowledge Электронное моделирование |
| description |
The paper presents general information about a notion “ontology” historical derivation. Apart from this different ways of ontology term transformation for usage in artificial intelligence systems are analyzed. Ontology is regarded there as a complex of knowledge for clear representation of the data about events, phenomena, general and special notions concerning society, laws and the world. Apart from this, ontology is developed to supply different information about the subject of interest. There are a number of ontologies, namely surface, top, domain ones and so on, which form a base for further development of knowledge based systems and their application in combination with artificial intelligence and a set of databases for improving the process of logical thinking and making relevant decisions. Ontologies are of particular importance for law and legal theory for rule formalization, accepting court resolutions and providing information about precedents and untypical cases. The ontology design criteria are also given along with the peculiarities of their application in legal domain. Ontologies are formed with specific goals, but there are no ways of forming their contents and design. The main task to be followed in ontology creation deals with the strict and clear formulation of the idea of ontology with allowance for the link between different ontologies. |
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Kosenko, S.O. |
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Kosenko, S.O. |
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Kosenko, S.O. |
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The main statements of ontology theory and its implementation in the system of legal knowledge |
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The main statements of ontology theory and its implementation in the system of legal knowledge |
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The main statements of ontology theory and its implementation in the system of legal knowledge |
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The main statements of ontology theory and its implementation in the system of legal knowledge |
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The main statements of ontology theory and its implementation in the system of legal knowledge |
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main statements of ontology theory and its implementation in the system of legal knowledge |
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Інститут проблем моделювання в енергетиці ім. Г.Є. Пухова НАН України |
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2018 |
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Применение методов и средств моделирования |
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https://nasplib.isofts.kiev.ua/handle/123456789/131245 |
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The main statements of ontology theory and its implementation in the system of legal knowledge / S.O. Kosenko // Электронное моделирование. — 2018. — Т. 40, № 1. — С. 95-113. — Бібліогр.: 42 назв. — англ. |
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UDK 004.91+004.81/34.01
S.O. Kosenko,
post-graduate student The Georgy Pukhov Institute
for Energy Modelling of NAS of Ukraine;
(Ukraine, 03164, Kyiv, 15, General Naumov Str.,
e-mail: sergey.a.kosenko@gmail.com)
The Main Statements of Ontology Theory
and Its Implementation in the System of Legal Knowledge
The paper presents general information about a notion “ontology” historical derivation. Apart
from this different ways of ontology term transformation for usage in artificial intelligence sys-
tems are analyzed. Ontology is regarded there as a complex of knowledge for clear representation
of the data about events, phenomena, general and special notions concerning society, laws and
the world. Apart from this, ontology is developed to supply different information about the sub-
ject of interest. There are a number of ontologies, namely surface, top, domain ones and so on,
which form a base for further development of knowledge based systems and their application in
combination with artificial intelligence and a set of databases for improving the process of logical
thinking and making relevant decisions. Ontologies are of particular importance for law and legal
theory for rule formalization, accepting court resolutions and providing information about prece-
dents and untypical cases. The ontology design criteria are also given along with the peculiarities
of their application in legal domain. Ontologies are formed with specific goals, but there are no
ways of forming their contents and design. The main task to be followed in ontology creation
deals with the strict and clear formulation of the idea of ontology with allowance for the link be-
tween different ontologies.
K e y w o r d s: ontology, law, artificial intelligence, conceptualization, domain of law.
Ïîäàíî çàãàëüí³ â³äîìîñò³ ïðî ïîõîäæåííÿ ïîíÿòòÿ «îíòîëîã³ÿ» òà ïðîàíàë³çîâàíî øëÿõè
éîãî ïîäàëüøî¿ òðàíñôîðìàö³¿ äëÿ âèêîðèñòàííÿ â ñèñòåìàõ øòó÷íîãî ³íòåëåêòó, äå
îíòîëîã³ÿ ðîçãëÿäàºòüñÿ ÿê êîìïëåêñ çíàíü, ùî íàäàþòü ïåâíó ³íôîðìàö³þ ïðî îá’ºêò
äîñë³äæåííÿ. Íà òåïåð³øí³é ÷àñ ðîçðîáëåíî íèçêó ð³çíîìàí³òíèõ îíòîëîã³é, à ñàìå ïî-
âåðõíåâ³, òîïîâ³, äîìåíí³ òîùî, êîòð³ º îñíîâîþ ïðè ðîçðîáö³ ñèñòåìè øòó÷íîãî ³íòåëåêòó
ç âèêîðèñòàííÿì íàêîïè÷åíèõ çíàíü òà áàç äàíèõ äëÿ óäîñêîíàëåííÿ ïðîöåñó ëîã³÷íîãî
ìèñëåííÿ ³ ïðèéíÿòòÿ â³äïîâ³äíèõ ð³øåíü. Îñîáëèâîãî çíà÷åííÿ íàáóëè îíòîëî㳿 â ïðàâî-
çíàâñòâ³ äëÿ ôîðìàë³çàö³¿ çàêîí³â, ïðèéíÿòòÿ ñóäîâèõ ð³øåíü òà íàäàííÿ ³íôîðìàö³¿ ïðî
ïåâí³ ïðåöåäåíòè òà íåòèïîâ³ âèïàäê³. Íàâåäåíî êðèòå𳿠äèçàéíó îíòîëîã³é, à òàêîæ
îñîáëèâîñò³ ¿õ çàñòîñóâàííÿ â ïðàâîâîìó äîìåí³. Ôîðìóâàííÿ îíòîëîã³é ìຠñïåöèô³÷í³
çàâäàííÿ, îäíàê â³äñóòí³ ÿê³-íåáóäü ñïîñîáè ôîðìóâàííÿ ¿õ çì³ñòó òà äèçàéíó. Ãîëîâíå
ISSN 0204–3572. Åëåêòðîí. ìîäåëþâàííÿ. 2018. Ò. 40. ¹ 1 95
� S.O. Kosenko, 2018
çàâäàííÿ, ÿêîãî ñë³ä äîòðèìóâàòèñÿ ïðè ñòâîðåíí³ îíòîëî㳿, ïîëÿãຠó ñóâîðîìó ³ ÷³òêîìó
ôîðìóëþâàíí³ ñóò³ îíòîëî㳿, âðàõîâóþ÷è çâ’ÿçîê ì³æ ð³çíèìè îíòîëîã³ÿìè.
Ê ë þ ÷ î â ³ ñ ë î â à: îíòîëîã³ÿ, ïðàâî, øòó÷íèé ³íòåëåêò, êîíöåïòóàë³çàö³ÿ, ïðàâîâèé
äîìåí.
General information about ontology. Representation of the complex of gener-
ally accepted knowledge is based on conceptualization combining objects into
the singe unit, concepts and other logical categories that simultaneously form
certain concept and thus, related between themselves with respective links and
relations [1]. Therewith the conceptualization itself is a speculative and simpli-
fied view on the real world, which we would like to image in one way or another.
Any knowledge basis and the system built on the accumulated knowledge as
well as the factors created with the help of certain knowledge subject to concep-
tualization explicitly or implicitly [2].
Ontology is the clear specification of conceptualization. It is known that this
word was borrowed from philosophy where ontology is considered as the sys-
tematized report of existence. At the same time the systems of artificial intelli-
gence consider existence as the ability to any kind of representation. When infor-
mation about a specific area of knowledge is represented in a declarative formal-
ism, the number of conceptual objects covered by it is called the totality of dis-
courses. Information about conceptual objects as well as existing interrelations
between themselves is located at the respective thesaurus of terms with the help
of which specially developed software familiarizes the user with that sphere of
knowledge presented in ontology. With this purpose a computer program repre-
sents ontology in the form of informative terms, and the definitions unite names
of the objects into the totality of discourses that are the classes, relations, func-
tions, etc., accompanied with text from which one can easily understand the
meaning of objects’ names and the restrictions imposed on the interpretation and
usage of terms. It is generally accepted that ontology is the provision of consis-
tent theory. Ontology is very often equated with taxonomic hierarchies of
classes, without defining the classes themselves and their relation to the classifi-
cation; however, ontologies actually represent a much broader category [3]. Be-
sides, ontologies cannot be limited by the conservative definitions only that are
such explanations in the sense of traditional logic that only introduce this termi-
nology but do not give any information about the things related to these terms.
For specification of conceptualization, firstly, it is needed to formulate axioms
that define possible ways of interpretation of the specified terms.
As a rule general ontologies are used to describe the tasks that are envisaged
for execution by every factor in such a way that they exchange information about
the discourse domain itself, and this might give the possibility to refrain from the
simultaneous application of all logic theory. The conclusion is that the factor be-
S.O. Kosenko
96 ISSN 0204–3572. Electronic Modeling. 2018. V. 40. ¹ 1
longs to ontology under the conditions of its real subordination to the require-
ments and definitions of ontology. The idea of ontology subordination is built on
the knowledge level perspective (KLP) that was formulated by Newell (1982);
according to it the factor is endowed with knowledge, in so doing it remains in-
dependent of the representative symbols system embedded in it [4]. Knowledge
is transferred to the factor with the purpose to follow up its actions. Due to this
the factor can «know» something only in case when it acts as if it knows certain
information, under these conditions its actions are logic and consistent for reach-
ing the final target. Action of the factors, including computers and servers, can
be observed with the help of the functional interface on the dialogue base «asser-
tion-question»; in its frame the user interacts with the factor asserting something
and simultaneously asking the question [5].
In practice, the general ontology defines the glossary of concepts and terms
with the help of which the exchange of questions and assertions between factors
is done. Usage of the glossary is done following ontological responsibilities that
by default are the agreements to use the unified glossary in the comprehensive
and consistent manner. Each of these factors using the unified glossary should
not mandatory have the entire sum of knowledge fixed in ontology. On the con-
trary, some factors give answers to one part of the questions arising from appli-
cation of the unified or consolidated glossary and the rest give answers to the
other one, so in this way they complement one another.
In other words, responsibilities of general ontology are the guarantee of har-
mony but in no way of exhausting in provision of information by means of questions
and assertions arising from usage of the glossary embedded in ontology.
Engineering criteria of ontologies. Ontologies do not originate by them-
selves, they are developed. When we put a target to represent something in on-
tology, we have to find certain design decisions. In order to control and evaluate
the design there should be objective criteria that are based on the concept of the
final artificial object but not on the priory concepts on naturalness or reliability.
In this regard Gruber [6] proposed the set of criteria for creation of ontologies;
their task is dissemination of knowledge and development of interaction be-
tween the programs based on mutual conceptualization. So, let us proceed to the
explanation of every criterion.
Clearness. Ontology must clearly transfer the notions embedded in every
term. Definitions should be objective and independent of the social context and
their complexity. There is the formalism that is used for this purpose. When the
definition can be presented in the form of the logic axioms so it should be done
like this. When it is possible the complete definition (assertion that is defined by
the necessary and sufficient conditions) prevails over partial definitions (asser-
tion that is defined as either necessary or sufficient conditions). All definitions
should be given in common language.
The Main Statements of Ontology Theory and Its Implementation
ISSN 0204–3572. Åëåêòðîí. ìîäåëþâàííÿ. 2018. Ò. 40. ¹ 1 97
Coherence. Ontology should be coherent that is it should sanction such con-
clusions that are in concord with the definitions. At least, the axioms in defini-
tions should be logically consistent. Coherence should also apply to the concepts
that are usually presented by the common language in documentation and exam-
ples. When the expression derived from the axiom contradicts the definition or
example, ontology should be considered incoherent.
Prevalence. Ontology must be developed in such a way that to foresee the
usage of the complex glossary. It must create a conceptual basis for the line of
expected tasks, in so doing these data presentation should be organized in such a
way that to give a possibility to expand deep the foundation ontology. In other
words, a user should have a possibility to determine new special purpose terms
using already existing complex glossary without revision of already existing in-
terpretations and concepts.
Coding minimum error. Conceptualization specification should be done at
the level of knowledge independent of the concept coding with the help of sym-
bols. Deviation from the embedded concept sense occurs when the choice of the
representation variants is developed only with the purpose of convenience for
indexing and realization. Coding error must be minimized as the same factors of
knowledge dissemination could be used in various representation systems and
diverse styles of information presentation.
Minimal ontological responsibility. Ontology should require minimum on-
tological responsibility sufficient for support of the planned activity of know-
ledge dissemination. Ontology should require minimum responsibility with re-
gard for the artificial intelligence under modelling, giving the possibilities to the
parties involved in the ontology to freely specify and illustrate ontology in the
way they need it. As the ontological responsibility is based on the consistent ap-
plication of complex glossary, so it can be minimized by elaboration of the
weakest theory (taking into consideration the biggest models) and giving expla-
nation only to the terms that have significant meaning for information transfer
related to the theory. Ontology is subjected to some other purpose than the sim-
ple data base and thus, its application is related to the attraction of completely
different concept of representative sufficiency. General ontology must evaluate
the glossary only for presentation of domain information, whereas the data base
can comprise information that is necessary for resolution of problems or presen-
tation of responses to any requests with regard for the domain.
Design trade-off decisions. Ontology design along with other problems re-
quires trade-off decisions between criteria. However, criteria, by their nature,
mostly do not differ. For example, with the purpose of clarity the definition must
restrict the possibility of term interpretation. Minimization of ontological re-
sponsibility in its turn means elaboration of the weak theory; under these condi-
S.O. Kosenko
98 ISSN 0204–3572. Electronic Modeling. 2018. V. 40. ¹ 1
tions the existence of other models is admitted. These two targets do not contra-
dict each other. Clarity criterion analyses the definition of terms while the onto-
logical responsibility is related to information on conceptualization. While ta-
king the decision that there are reasons for distinction, an immediate brief expla-
nation of differences should be done.
Another apparent contradiction exists between the prevalence and ontologi-
cal responsibility. Ontology that envisages fulfilment of the tasks line does not
contain complex glossary that is sufficient for providing of information about all
these tasks. Thus, there is the necessity to increase responsibility in compliance
with the expansion of complex glossary. Resilient ontology that is the one which
can expand and increase can specify very general theory but contain the repre-
sentative apparatus for determination of needed specializations.
Both the ability to expand as well as the ontological responsibility com-
prises the concept of sufficiency or adequacy [7].
In traditional data modelling the ontology is usually defined with the help of
information messages or data base diagram. As the purpose is the preparation
and writing of distinct information that do not depend on specific data or pro-
gramming language, the KIF (knowledge interchange format) is used [8]. Each
ontology defines the complexes of classes, interrelations, functions and sizes of
the objects for any discourse domains, it also comprises axiomatising in order to
restrict interpretation. The communication language that arises under such con-
ditions is a domain-specific specification of conceptualization.
Peculiarities of ontologies. Trends of ontologies applications are various.
This determines the difference of functions that ontologies perform according to
each trend. Due to this, while engineering ontology, one should take into consid-
eration the trend where the ontology is planned for application as well as its role
in attainment of the ultimate goal. Here, one should analyse the most important
ontology functions [9].
Obtaining knowledge is a rather complicated process, although it is critical
for engineering knowledge-depended systems. It was considered for a long time
that problems precisely arising from knowledge obtaining are the stumbling
rock for wide application of the systems built on knowledge. It is clear that the
quality of such systems to a large extent is determined by the knowledge that is
embedded it them. The task should be executed in a systemic way, in so doing
the obtained knowledge is arranged in coherent structure.
Ontology that can be applied for specification of knowledge base creates
very convenient basis for realization of knowledge obtaining process.
The structure for knowledge obtaining should have the following features:
It informs us about the thing we need in order to obtain the knowledge about
something and also what specific knowledge we need to receive about specific
things and what type of information about them we can ignore.
The Main Statements of Ontology Theory and Its Implementation
ISSN 0204–3572. Åëåêòðîí. ìîäåëþâàííÿ. 2018. Ò. 40. ¹ 1 99
Ontology structure creates the grounds for data acquisition process.
Ontology determines errors in the obtained knowledge that must be cor-
rected and also incompleteness in knowledge that must be compensated. Under
these conditions ontology must clearly identify when the knowledge obtaining
process reaches its culmination.
Ontology can be used to search for inconsistencies and uncertainties. It is es-
pecially useful when several experts do not have a clear view on future usage of
the domain.
Very good example of ontology intended for obtaining knowledge is Pro-
tege that is widely used in various software applications [10].
Knowledge dissemination. The second important function of ontologies is
the knowledge dissemination between applications. It often happens that several
applications impose common requirements upon knowledge. That is why due to
complexity of data acquisition the possibility to use knowledge that is already
presented in other application seems very perspective. However, there is the dif-
ficulty because we do not have any guarantees that the knowledge will be pre-
sented in the same way. For example: thus, a knowledge base can have a parent
with three family relationships and indicate a father or mother by their place in
family relationships. Other knowledge base can have for every person two fa-
mily relations and distinguish the fathers from the mothers by applying logical
conditions to sex determination. To make the knowledge dissemination happen
inconsistencies must be settled. This, in its turn, requires availability of glossary
description in the applications that are given by ontology. Given this expected
ontology will supply presentation harmonization means of different knowledge
bases. That is why the role of ontologies is analogue to the integration diagram in
the data base. Typical example of the knowledge dissemination is systemic com-
puter web that has concentrated a great deal of contemporary ideas on ontology.
Reusage of knowledge. The problems which are quite similar to those en-
countered in the dissemination of knowledge also occur in cases when we want
to apply a system of knowledge that has been developed for one application to
another one. Here the problems can be lesser as we do not try to receive at any
cost an access to the knowledge available in other system and vice versa, trying
to adapt the existing data base to our requirements. However, we really require
that the suppositions used in the design and reduced meanings of the terms will
be easily accessed and therewith the needed documentation on this issue must
also be present in the ontology [11].
Verification and validation. A lot of systems based on the verification and
validation of knowledge are simply built on the verification procedures imposed
by the experts or, besides of this, on experts’ ideas regarding the perspective of
final input data obtaining. Principal verification and validation require availabi-
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lity of several independent specifications; with their regard the system can be
verified and accepted. There are adduced arguments, that the use of such func-
tion by ontologies can occur through clear delineation of the system objectives
and attribution to them an objectivity nature [12].
The systems based on a certain body of knowledge are often built not only
with the aim of wide application but with an attempt to understand the processes
of artificial logical thinking of a particular domain. At the same time it is often
very important to understand the system thoroughly by means of its self-actua-
lization. It is also equally difficult to make an intelligent comparison of two im-
plementations based on different approaches. However, if we have the descrip-
tion of such systems in terms of their ontologies then, the understanding and
comparison can be done on the level of conceptualization. This can be possible,
since under such conditions discrepancies in the approaches become rather ex-
plicit, and this facilitates the explanation of their weak and strong features in
case of intellectual analysis of court cases through the prism of current legisla-
tion. This level is useful for comparison, for example, of the factors that are used
in CATO [13] or sizes with HYPO [14], or comparing CATO with the changes
that depend on the usage of certain rules that have been proposed by Prakken and
Sartor [15]. Ontology engineering with the purpose of suppositions that are the
base of system realization gives the possibility to clarify to some extent the un-
derstanding of domain in the limits of which it operates.
Types of ontologies. Within the limits of basic definition of ontologies
there are a lot of possibilities for various modifications that is quite natural under
the terms of potential variation existence in motivations. It is on this basis that
different types of ontologies originate [16, 17].
Surface ontologies. The simplest variant out of all when the ontology is
built of the line of hierarchy placed terms. Such ontology can resemble the refer-
ence book similar to one that is used in the information-and-search system for al-
ready long time. The task of such ontology is mainly to assist in information
search and therewith augmentation or reduction of the data base able to create hie-
rarchy structure depending on the bigger or lesser quantity of the answers that
can be obtained for the initial request. The Word net [18] is the most famous and
wide spread ontology of this type.
Higher or upper ontologies. Higher or upper ontology tries to describe fun-
damental categories that can be applicable to all domains. In this sense it be-
comes a hierarchical peak and being engineered has the purpose to make evident
and comprehensive certain domain conceptions that belong to the set of funda-
mental categories. Usually, upper ontology begins from such a category as, for
example, the “thing” and then gradually goes down through such categories,
namely, tangible or intangible things but stops not far from the special things, in-
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cluding machines. Events, persons and relations as well as, perhaps, concepts
that have relation to the time and action may likely be found in such ontology.
CYC is the most famous ontology of such type [19].
Domain ontology. Domain ontology is directed at creation of concepts that
are fundamental for a certain domain. Thus, in the sphere of law we usually ex-
pect the ontology to contain such concepts as the law, legal entity and rule.
Several such ontologies are known that have been specially designed for ap-
plication in the sphere of law. Here, first of all, it is worth mentioning the
Valente’s functional ontology [20] and frame-based ontology of Van Kralingen
and Visser [11, 21].
Attachment ontologies. Attachment ontology contains very detailed and
specific concepts that give the possibility to perform a separate task within the
limits of defined fragment in the sphere of law. Such ontology will contain such
motions as, for example, “break in the employment”, “employer”, “employee”,
etc. In legal domain such ontologies are usually called law-specified ontologies
because they provide information about concepts and notions that are used in the
specific law. Visser (1995) gives the example of such ontology for the domain
related to the benefits of unemployed in Holland [11].
Attachment ontology can be considered as the foundation for creation of hi-
erarchy, which then must serve for evolution of upper ontology into the domain
one. The latter can be used for transformation into the attachment ontology.
Usually attachment ontologies contain details of features, values and axi-
oms that are absent in the surface ontologies although they, as a rule, contain in-
formation about all these three information levels [9].
Ontologies of the artificial intelligence for the law. McCarty developed
the Language of Legal Discourse (LLD) that can be considered the first try to of-
fer ontology of the artificial intelligence in the law [22]. The purpose of the de-
velopment was a creation of the language that can mirror the structure of the le-
gal language and thus was suitable for representation of legal knowledge. The
language comprised together with the atomic formula a lot of types of horn type
law acts, inserts of ancillary claims that comprise conclusions, objections, and
rules of silent consent (tacito consensu), prototypes and inconsistencies. In addi-
tion the proposed program had a radical feature allowing consideration of the
representative features problem, including those having certain relation to time,
actions or deontic concepts with regard for permission and obligation.
Thus, this language had a lot in common with the upper ontology. Analysing
law concepts McCarty stressed that their definitions were not sufficiently pro-
found because the law concepts were not completely formed, were changing
with time and were improved during the theory development. In this respect, he
conceptualized law concepts, using therewith a set of mandatory invariant con-
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ditions, set of concept types, and set of methods in order to transit from one type
of concept to another. In order to understand how the LLD can be changed into
the domain ontology one should pay attention to the variant of using LLD for
representation of the concept for the property right [23].
The “Norm” developed by Ronald Stamper was the other early try to use
formalism for creating the ontological basis for the law [24]. The author hoped
that the offered formalism could be applied in the social system models for
which he saw only one law. The Norm has three important ontological concepts:
factors modify and regulate real world by means of actions for which they
are responsible;
behavioural variants are associated with the factors and characterise them;
realizations are certain conditions that are created by execution of certain
actions.
Despite the greater perfection of the mentioned approach its application in
the artificial intelligence systems in the law was limited.
Legal ontology domains filled with concrete content started to be created in the
mid-nineties of the twentieth century. Among them certain place was occupied by
the mentioned above functional [20] and frame-based ontology [21]. Both are pre-
sented in the moderate quantity of details and formalized with the help of the same
language for description of ontologies called “ONTOLINGMA” [25].
In Valente’s functional ontology the law is understood as a tool for a change
or behaviour modification with the purpose of social demands implementation.
He differentiated six categories of the law knowledge:
1) Informative knowledge that ascribes an informational status to the situa-
tions by the binary principle: prohibited or mandatory. It should be mentioned
that just these two conditions of the cases are considered here the objects of
deontic functions, and the actions that realize them, originate from them by their
normative status.
2) Knowledge of the real world describes the real world that is controlled
with the help of the terms and normative knowledge and in this way can be con-
sidered as the interface between (not legal) general knowledge content and nor-
mative knowledge.
3) Obligatory knowledge: this is such knowledge that gives the possibility
to violate the norms that are defined for certain factors.
4) Reaction knowledge is the knowledge that describes the sanctions that
can be applied to those responsible for the norm violation.
5) Meta-legal knowledge describes the way how the legal knowledge should
be justified. For example, this knowledge usually comprises the principles of the
special law to help in the contradiction resolving in the legal knowledge.
6) Creative knowledge is envisaged for explanation of how the concept of
legal knowledge occurs and disappears.
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This ontology creates the basis of “on-line” or “active mode” systems,
which Valente describes as the “Legal information server” [26]. The main fea-
ture of the system is the storage of information in the form of the tests and with
the help of analysis fulfilment system that are interrelated between themselves
by general expressions in terms of functional ontology. Key direction of this
conceptualisation is to create organizational and concordance principles of legal
knowledge especially from the position of the conceptual search. Valente men-
tions two restrictions for ontology realization. The first is purely practical and re-
lated to the fact that modelling with the purpose of this concept promotion is re-
source consuming, namely requires simultaneous attraction of huge amount of
auxiliary means. At the same time it should be recognized that the description of
various types of the legal knowledge is presented rather comprehensively, ho-
wever, the domain model in the system of the active mode “on-line” is rather
limited. The second ontology limitation is related to the theoretic aspect namely
its ability to specify properly various changes in the legislation and thus adapt to
them in a certain sense.
In this connection Valente expressed the following idea: “While all expect
that ontology can adequately represent legal knowledge in several spheres of
legislation and legal systems, this issue has not yet been tested in practice”. Be-
sides, it is not clarified how the functional ontology is consistent with all types of
questions that are covered in the legislation. At the same time Valente persuades
himself that the ontology can and must serve as the connective line between the
theory of the law and the legislation built on the artificial intelligence creating a
neutral and independent of any problems medium with the help of which the law
theory ideas can be formulated. Due to this, Valente evaluates the strength of the
functional ontology as: “Legal argumentation model put into this ontology only
with the little likelihood can become a cognitive factor but seems to be illogical
and complicated for understanding for an ordinary reader as well as for theoreti-
cian of the legal knowledge. Instead, the tasks of ontology are not the visibility
improvement, facilitation of perception and explanation of empirical facts of ju-
dicial decisions, but on the contrary, search for the most economical way to rep-
resent legal knowledge and its use for proper argumentation” [27, 28].
From the last citation it is clear that the task of ontology is, first of all, related
with the computerization and creation of the theoretical ground for implementa-
tion of computer technologies but not the concordance with the theory of legal
knowledge. It is exactly from this perception the functional ontology should be
evaluated.
Frame-based ontology of van Kralingen and Visser was created with the
purpose of approach improvement to the development of computer information
systems in legislation and, in particular, for improvement of effectiveness in
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multiple application of knowledge specifications by reducing their subordina-
tion to the specific task. This ontology differs from the ontology designed to be a
universal one for the whole legislation as well as law-specific ontology con-
taining concepts with regard to the separate law domain, although, of course,
these two ontologies are related between themselves whereas the law-specific
ontology can be considered as the detailed general ontology that adapts it to the
specific domain [21].
The general ontology divides the legal knowledge into three logic catego-
ries: norms, acts and concepts description. For each category the ontology deter-
mines the frame structure where all specific attributes of a certain category are
listed. Three types of categories together with the specific attributes of some cat-
egories in general comprise a unified whole that retains its value and validity for
each part of legislation. However, the modelling of legal sub-domain also re-
quires solution of many ontological issues related to the peculiarities of legal
problems content of this specific domain. In this regard it is important to note
that the law-specific ontology consists of information messages of specific con-
tent that have direct relation to the specific law domain and which are used to illust-
rate the way how the norms, acts and concepts are formed in this law domain. Given
all the above, it comes out that the general component can be reused in any of the
legal domains, whereas the components of the law-specific ontology can be reused
only for execution of various tasks in the limits of this specific domain.
This ontology was used as the basis of FRAMER system for consistent ap-
plication of the Dutch law of unemployment benefits by means of two opera-
tions. During the first stage there is the determination of possibility to render
benefits for unemployed with the help of specific classification test, at the sec-
ond stage there is the planning of the line of actions needed to be implemented in
order to reach specific legal decisions [11, 29].
The considered second legal ontology also has its drawbacks. Firstly, it is a
resource intensive one because it requires simultaneous usage of many auxiliary
means, although it is justified by its genericity. Secondly, genericity with regard
to the legislation does not make this ontology an obligatory general for other do-
mains. Thirdly, there is the logic question how firm is this ontology linked to the
theory of law. Exactly the fact that it is based on the analysis of the theory of law
and examines some parts of the legislation imposes a demand to be at least par-
tially agreed with the theory of law.
Thus, two considered ontologies are very different, and at the highest level
this divergence has a specific feature. This happens because the ontologies have
different final targets in which they start their conceptualization. Thus, in his
functional ontology Valente looks for the ways to put into parts the whole legal
system, while in the frame-based ontology van Kralingen and Visser seek for
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special building blocks out of which it is proposed to build the law as an object of
knowledge. Having such a diversion it is quite natural to know whether any fur-
ther comparison of these ontologies is appropriate. If, however, delve a little bit
further, there is the existence of something in common. Thus, in the frame-based
ontology we can find «event classification» and «process classification» as the
defined relations. The task of these relations is the concordance of the real event
with its presentation in legal documents.
For example, if A physically kills B, so these events are highlighted in the
normative description as A performs the murder of B. Exactly this relation be-
tween the physical act and departmental description is the object of such cate-
gory of functional ontology as the “world knowledge”. With this in mind, we can
see that in two conceptualizations the transition from the ordinary description of
the physical act to its description in the language of legal departmental docu-
ments is of great importance. So the normative status by two ontologies is de-
fined as a function although in the functional ontology it starts from the situation
and is finished by the normative status, and in the frame-based ontology it origi-
nates from the approved nor.s. and later transfers into the normative status. Be-
side, for the former ontology the availability of three peculiarities of normative
status is a peculiar feature, whereas in the latter ontology two such concepts as
“permitted» and “not declared” are referred to the concept “not violated”. Ne-
vertheless, there is the possibility of transition to “situation” of the former onto-
logy from the “act” of the latter ontology built on the basis of the imperfectly
developed normative documents where the crack for further act improvement
is left [29].
The described two ontologies have become the predecessors of modern
ontologies of the artificial intelligence in the law. Among them one should note
the Norm that had been developed thanks to the project “Network” that was ful-
filled for Italian Investigation Council and the Ministry of Justice [30]. This is
the example of the surface ontology that is designed for support and control of a
special legal language since the development of standard terminology can con-
tribute more efficient information search of legal knowledge.
“E-Court” ontology [31] was created in the Amsterdam University and is
the upper ontology targeted at the Dutch Criminal Code. This detailed ontology
is specially designed with the purpose of information provision about the re-
quirements for preparation of legal documents in the courts.
Perhaps, the most perfected ontology, which is the excellent example of on-
tology application while development of law information systems is the “E-Po-
wer” [32] that has been developed within the limits of the project for the Dutch
tax organization for incomes”. This ontology supports all stages of legal activity
starting from the design, which then transit to publication and reaches its culmi-
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nation after application in the penalty system of legislation compliance monito-
ring. This is done with the help of unified network of ontological models net-
work. Ontology gives opportunities to define the drawbacks and inconsistencies
already at the design stage that proves its great potential.
Also, as the example of specific ontology application iPRONTO should be
mentioned [33]. This ontology is developed with the purpose of programming
factor support regarding to the legal control. iPRONTO is developed on the
SUMO that derives from the upper ontology IEEC and acquires its specific fea-
tures in the frame of the Intellectual property law developed by the World Intel-
lectual Property Organization.
Analysis of existing knowledge through the light of ontological views.
Both functional and frame-based ontologies were developed as the predecessors
for engineering of knowledge basis with the purpose of its further application
based on the usage of certain software and hardware. In order to understand
which ontology has certain advantages for support of existing knowledge in the
law domain, firstly, one should understand and analyze peculiarities of classic ap-
proaches applied in development of artificial intelligence in law. This can be illus-
trated on the basis of logic programming method consideration that is most clearly
represented in the formalization of the British Nationality Law (BNL) [34].
The knowledge base in this case consists of the clearly formulated sentences
of the following shape À � Â1 _ _ _Ân, which should be interpreted as À is true
if all  – Â1… Ân are also true. An argument to represent the law in this way is
that the majority of legislations are deterministic in their nature, and therefore
expanded clear statement can form simple and correct language form, through
which it will be possible to characterize accurately the legal definition (Sergot,
1991) [35]. The concept of “expansion” here means that the right parts of
sentences may contain literals as “no Bi”, and such objections are interpreted as
failure. The consequence is that while perception of certain decision involves
sufficiency of conditions for its main part the set of all interpretation for this
main part is used for provision of necessary condition for the truth of the state-
ment that is embedded into the main part of the sentence. Due to this there were
attempts to separate the cases where the instances were either desired or not de-
sired [36]. However, only in the program of BNL the parts of the sentences re-
mained unchanged.
Thus, for the above approach the legal knowledge is represented as a set of
definitions, and the definition is implemented as a series of extended clearly de-
fined subordinate clauses designed to provide individually sufficient and collec-
tively necessary conditions for the given concept.
Complete reconstruction of ontology usually requires specification of all
possible assertions including also the revision of their interpretations. In their
turn, assertions create two natural groups. The first one has definitions in the
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knowledge base and the second one does not have such definitions. The latter
can subdivide into the definitions that are provided by the system itself (for ex-
ample, today’s date) and the ones, which are presented by the user as the answer
to the question (for example, date and place of birth). Definition availability in
informative knowledge base depends on whether the assertion in the law is used
for formalization or not.
Such method of conceptualisation envisages several things [9]. Firstly, it
stresses that the conceptualisation is not obligatory general but at the same time
it is sufficient to be applied for various types of the law. Secondly, it operates on
the level of departmental domains. Due to this, at formalization of the law, for
example, BNL assertions that are provided to the user are the information about
physical actions that are later agreed with the respective program with the de-
partmental decisions. For other types of the law unspecified terms can be re-
ferred to the departmental information. All depends on the software abilities to
manipulate the definitions. When we attempt to expand formalization adding to
it an acquisition of the expert experience in order to facilitate separation and de-
finition of assertions as it has been done in the work [37], so in this case we leave
the original conceptualisation and we should admit this. Thirdly, one should em-
phasize the limited content of the applied assertions. If we accept that this is not
always correct (for example, when definitions are presented in the form of as-
sumptions) so we have to perform additional expansions. In this connection, one
must remember the criticism expressed by McCarty [38] who wrote:
Legal concepts cannot be adequately represented with the help of assertions.
On the contrary, legal concepts are unchanged and openly structured.
Legal laws are not static, but rather dynamic. Due to this, it is not important
to apply the theory for the process of legal decision adoption, but it is rather im-
portant to build one.
In the process of theory development the correct answers are absent al-
though credible arguments with various powers of conviction for every alterna-
tive revision of the law can exist that are used in atypical situations.
We can agree with all thoughts expressed by McCarty. However, since the
conceptualization in BNL is intended to be applied only to the terms that were
clearly defined in the legislation, it was possible here to compete and adduce a
counterargument that the concept proposed by this author was a limitation of ap-
proach application and not its denial due to the failure to be applied to the spe-
cific law. In his turn, McCarty offered alternative conceptualisation that in-
cluded three components of the legal concept.
The first one is linked to the set of necessary conditions, set of samples that
represent sufficient conditions as well as the set of transformations, which dem-
onstrate relationship between the samples. The first set can be empty, the second
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and the third sets as a rule are open and liable to expansion. As a result, it be-
comes possible to adapt the structure open to improvement by adding a dynamic
aspect, because “application of the concept to a new irregular situation automati-
cally changes the very concept” as the result of expansion of a set of standard
samples and/or a set of transformations. Under this interpretation it becomes
possible to interpret McCarty’s ideas i.e. not such that are an argument for
cancelling BNL-type approach, but rather for its improvement, because it pro-
vides the first component and the other two keeps in reserve. Any attempt to re-
construct ontologies in this way allows one to better understand the approaches
themselves as well as find out differences between the similar approaches like
the BNL approach and the approach that permits introducing additional informa-
tion from the experts’ experience and the cases of specific decisions. This also
helps to deepen our understanding of relationship between the approaches oppo-
site in their nature.
Conclusions. So, the ontologies are developed with the certain purpose. As-
sessment of ontology’s adequacy or its compliance to certain tasks can be done
only in case if the goal is specified. Criteria to which the ontology should comply
in order to create the foundation for knowledge representation are much stricter
under the terms of completeness and elaboration in comparison to those that are
designed to characterise an approach to the legal knowledge system with the
purpose of work contextualization.
There are no agreements with regard to what should be specified in the legal
ontologies and what specific details should be emphasized. Provided that there
are a number of tasks for each subsequent ontology for their further implementa-
tion (in terms of actions and sub-domains) we should expect significant differ-
ences in what is each ontology. The most important thing in engineering and ap-
plication of ontology is reliable knowledge on its designation. Besides, one
should avoid ontologies with other target that is not embedded into their essence.
The authors submit different conceptualization of the legal domain even
when their intentions and ideas are quite similar. In this case there is a quite natu-
ral question: “What ontology is true and which one is preferred?” However, this
question should be omitted whereas for ontology it’s not relevant. The main
thing, they must be clearly formulated. Secondly, there must be the possibility to
unite them and to define certain relations between them. And thirdly, there are
prerequisites for understanding what exactly caused the differences between
ontologies. It should be admitted, there is no agreement on basic approaches to
creation of legal domain conceptualizing, therefore, it is necessary to seek out
and try different alternatives.
Ontologies create useful basis for comparison and analysis of various ap-
proaches in the field of artificial intelligence application in legal studies. In this
regard, the work [39] indicates that the developers of computer technologies and
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systems must first of all study the theoretical basis of the domain design, under
which their developed system is engineered.
There is a certain compromise between the possibilities of the domain multi-
ple usage for fulfilment of various tasks in legislation and epistemological com-
pleteness, i.e the ability to cover all terms similar in content. At the lowest level
of detailing the ontology must be subordinate to the specific task under fulfil-
ment as well as to the completeness of information provided to the user perfor-
ming this task.
Differences between the law-specific and universal legal ontology must
necessarily exist. In this case the first ontology expresses the basic domain con-
ceptualization and the second one ensures implementation of engineering fin-
dings into efficient actualization of computer technologies in legislation.
Studies show that one and the same domain can be conceptualized by various
methods. Each method of domain conceptualization causes occurrence of a unique
ontology and none of them should have certain advantages over others.
At the present stage the studies focus on a modular approach in the develop-
ment of legal ontologies, which includes the simultaneous creation of the whole
library of ontologies [40-42].
The library of ontologies must contain competing ontologies developed
with the only purpose but subject to fulfilment different tasks. Besides, the lib-
rary must have a place for the so called auxiliary ontologies, which may be united
with obtaining a complex ontology. The library must comprise ontologies on
various abstraction levels in such a way that elaborated ontologies could be used
jointly with more abstract ones. Creation of such a library of ontologies will be-
come a basis for information exchange in legislation between various groups of
scientists and for checking of application effectiveness of certain ontologies in the
general system of artificial intelligence.
REFERENCES
1. Genesereth, M.R. and Nilsson, N.J. (1988), Logical foundations of artificial intelligence,
Morgan Kaufmann Publishers, California, USA.
2. Fulton, J.A. (1992), Standards working document ISO TC184/SC4/WG3 N103, IGES/PDSES
Organization, Dictionary/Methodology Committee, USA, Technical report on the semantic
unification meta-model.
3. Enderton, H.B. (1972), A mathematical introduction to logic, Academic Press, San Diego,
USA.
4. Newell, R.R. (1982), The knowledge level, Artificial Intelligence, Vol. 18, no. 1, pp. 87-
127, HYPERLINK «https://doi.org/10.1016/0004-3702%2882%2990012-1" \o »Persistent
link using digital object identifier" \t «_blank» doi: 10.1016/0004-3702(82)90012-1.
5. Levesque, H.J. (1984), Foundations of a functional approach to knowledge representation,
Artificial Intelligence, Vol. 23, no. 1, pp. 155-212, available at: HYPERLINK «https://
doi.org/10.1016/0004-3702%2884%2990009-2" \o »Persistent link using digital object iden-
tifier" \t «_blank» doi: 10.1016/0004-3702(84)90009-2.
S.O. Kosenko
110 ISSN 0204–3572. Electronic Modeling. 2018. V. 40. ¹ 1
6. Gruber, T.R. (1995), Toward principles for the design of ontologies used for knowledge
sharing, Int. Journal of Human-Computer Studies, Vol. 43, pp. 907-928, available at:
HYPERLINK «https://doi.org/10.1006/ijhc.1995.1081" \o »Persistent link using digital ob-
ject identifier" \t «_blank» doi: 10.1006/ijhc.1995.1081.
7. McCarty, L.T. (2002), Ownership: A case study in representing legal concepts, Artificial In-
telligence and Law, Vol. 10, pp. 135-161, doi:10.1023/A:1019584605638.
8. Genesereth, M.R. and Fikes, R.E. (1992), Knowledge interchange format, Version 3.0, Refe-
rence Manual, Tech. Rep. Logic-92-1, Computer Science Department, Stanford University,
USA, doi: 10.1.1.54.8601.
9. Bench-Capon, T.J.M. and Visser, P.R.S. (1997), Ontologies in legal information systems;
the need for explicit specifications of domain conceptualisations, Proceedings of the Sixth
International Conference on Artificial Intelligence and Law (ICAIL ’97), Melbourne, Aus-
tralia, 1997, pp.132-141, doi: HYPERLINK «https://doi.org/10.1145/261618.261646" \t »_
self" 10.1145/261618.261646.
10. Noy, N.F., Fergerson, R.W. and Musen, M.A. (2000), The knowledge model of Pro-
tege-2000: Combining interoperability and flexibility, Proceedings of the 12th International
Conference on Knowledge Engineering and Knowledge Management (EKAM 2000), Juan-
les-Pins, France, 2000, pp. 87-98, doi: 10.1007/3-540-39967-4_2.
11. Visser, P.R.S. (1995), Knowledge specification for multiple tasks, Kluwer Law International
Hague, Boston, USA.
12. Bench-Capon, T. and Jones, D. (1999), PRONTO: Ontology based evaluation of knowledge
based systems, Validation and verification of knowledge based systems, Eds A. Vermesan
and F. Coenen, Kluwer Academic Publishers, Dordrecht, Boston, USA, doi: 10.1007/978-1-
4757-6916-6_7.
13. Aleven, V. (1997), Teaching case based argumentation through an example and models,
PhD Thesis, The University of Pittsburgh, Pittsburg, USA, doi: 10.1.1.203.1165.
14. Ashley, K.D. (1990), Modeling legal argument, MIT Press, Cambridge, MA, USA.
15. Prakken, H. and Sartor, G. (1998), Modeling reasoning with precedents in a formal dialogue
game, Artificial Intelligence and Law, Vol. 6, pp. 231-287, doi: 10.1007/978-94-015-9010-5_5.
16. Ashley, K.D. and Bridewell, W. (2010), Emerging AI and law approaches to automating
analysis and retrieval of electronically stored information in discovery proceedings, Artifi-
cial Intelligence and Law, Vol. 18, pp. 311-320, doi: 10.1007/s10506-010-9098-4.
17. Bench-Capon, T. and Sartor, G. (2003), A model of legal reasoning with cases incorporating
theories and values, Artificial Intelligence, Vol. 150, pp. 97-143, doi: HYPERLINK «https://
doi.org/10.1016/S0004-3702%2803%2900108-5" \o »Persistent link using digital object
identifier" \t «_blank» 10.1016/S0004-3702(03)00108-5.
18. Miller, G.A., Beckwith, R., Fellbaum, Ch., Gross, D. and Miller, K.J. (1990), Introduction to
WordNet: an on-line lexical database, International Journal of Lexicography, Vol. 3, no. 4,
pp. 361-373, doi: 10.1.1.105.1244.
19. Guha, R.V., Lenat, D.B., Pittman, K., Pratt, D. and Shepherd, M. (1990), CYC: A midterm re-
port, Communications of the ACM, Vol. 33, no. 8, pp. 345-357, doi: 10.1080/08839519108927917.
20. Valente, A. (1995), Legal knowledge engineering: A modelling approach, IOS Press, Ams-
terdam, Netherlands.
21. van Kralingen, R., Visser, P.R.S., Bench-Capon, T.J.M. and van der Herik, J. (1999), A prin-
cipled methodology for the development of legal knowledge systems, International Journal
of Human Computer Studies, Vol. 51, no. 6, pp. 1127-1154, doi: HYPERLINK «https://
doi.org/10.1006/ijhc.1999.0300" \o »Persistent link using digital object identifier" \t «_blank»
10.1006/ijhc.1999.0300.
22. McCarty, L.T. (1989), A language for legal discourse I. Basic features, Proceedings of the
Second International Conference on Artificial Intelligence and Law, New York, 1989,
pp.180-189, doi: HYPERLINK «https://doi.org/10.1145/74014.74037" \t »_self" 10.1145/
74014.74037.
The Main Statements of Ontology Theory and Its Implementation
ISSN 0204–3572. Åëåêòðîí. ìîäåëþâàííÿ. 2018. Ò. 40. ¹ 1 111
23. McCarty, L.T. (2007), Deep semantic interpretations of legal texts, Proceedings of the Eleventh
International Conference on Artificial Intelligence and Law, Stanford, CA, 2007, pp. 217-224,
doi: HYPERLINK «https://doi.org/10.1145/1276318.1276361" \t »_self" 10.1145/ 1276318.
1276361.
24. Stamper, R.K. (1991), The role of semantics in legal expert systems and legal reasoning, Ra-
tio Jurist, Vol. 4, no. 2, pp. 219-244, doi: 10.1111/j.1467-9337.1991.tb00094.x
25. Gruber, T.R. (1992), ONTOLINGUA: A mechanism to support portable ontologies, Knowledge
systems laboratory, Tech. Rep., Stanford University, California, USA, doi: 10.1.1.34.9819.
26. Valente, A. and Breuker, J. (1999), Legal modeling and automated reasoning with ON-LINE, In-
ternational Journal of Human-Computer Studies, Vol. 51, no. 6, pp. 1079-1125, doi:
HYPERLINK «https://doi.org/10.1006/ijhc.1999.0298" \o »Persistent link using digital ob-
ject identifier" \t «_blank» 10.1006/ijhc.1999.0298.
27. Valente, A. (2006), Types and roles of legal ontologies, Law and Semantic Web. LNAI 3369,
Ed V.R. Benjamins, Springer Verlag, Berlin, Germany, doi: 10.1007/978-3-540-32253-5_5.
28. Valente, A. and Breuker, J. (1994), A functional ontology of law, Towards a global expert
system in law, Eds. G. Bargellini and S. Binazzi, CEDAM Publishers, Padua, Italy, doi:
10.1.1.39.8951.
29. Visser, P. and Bench-Capon, T. (1998), A comparison of four ontologies for the design of le-
gal knowledge systems, Artificial Intelligence and Law, Vol. 6, no. 1, pp. 54-68, doi:
10.1023/A:1008251913710
30. Bolioli, A., Dini, L., Mercatali, P. and Romano, F. (2002), For the automated mark-up of
Italian legislative texts in XML, Legal Knowledge and Information Systems JURIX 2002:
The Fifteenth Annual Conference, Eds. T. Bench-Capon, A. Daskalopulu, and R. Winkels,
IOS Press, Amsterdam, Netherlands, pp. 21-30, doi: 10.1.1.106.6559.
31. Breuker, J., Elhag, A., Petkov, E. and Winkels, R. (2002), Ontologies for legal information
serving and knowledge management, Legal Knowledge and Information Systems JURIX
2002: The Fifteenth Annual Conference, Eds. T. Bench-Capon, A. Daskalopulu, and R. Win-
kels, IOS Press, Amsterdam, Netherlands, pp. 73-82, doi: 10.1.1.59.1956.
32. van Engers, T.M., Gerrits, R., Boekenoogen, M., Glassee, E. and Kordelaar, P. (2001),
POWER: using UML/OCL for modeling legislation - an application report, Proceedings of
the 8th International Conference on Artificial intelligence and Law, ACM Press, New York,
USA, pp. 157-167, doi: HYPERLINK «https://doi.org/10.1145/383535.383554" \t »_self"
10.1145/383535.383554.
33. Delgado, J., Gallego, I., Lorente, S. and Garcia, R. (2003), iPRONTO: An ontology for digi-
tal rights management, Legal Knowledge and Information Systems JURIX 2003: The Six-
teenth Annual Conference, Ed. D. Bourcier, IOS Press, Amsterdam, Netherlands, pp. 111-
121.
34. Sergot, M.J., Sadri, F., Kowalski, R.A., Kriwaczek, F., Hammond, P. and Cory, H.T. (1986),
The British nationality act as a logic program, Communications of the ACM, Vo1. 29, no. 5,
pp. 370-386, doi: HYPERLINK «https://doi.org/10.1145/5689.5920" \t »_self" 10.1145/5689.
5920.
35. Sergot, M.J. (1991), The representation of law in computer programs, Knowledge Based Sys-
tems and Legal Applications, Ed. T.J.M. Bench-Capon, Academic Press, London, UK.
36. Kowalski, R.A. (1989), The treatment of negation in logic programs for representing legisla-
tion, Proceedings of the Second International Conference on AI and Law, ACM Press, New
York, USA, pp. 48-69, doi: HYPERLINK «https://doi.org/10.1145/74014.74016" \t »_self"
10.1145/74014.74016.
37. Bench-Capon, T.J.M. (1991), Practical legal expert systems: the relation between a formali-
sation of law and expert knowledge, Computers, Law and AZ, Eds. J. Bennun and M.
Narayanan, Ablex, New York, USA, pp. 191-201.
S.O. Kosenko
112 ISSN 0204–3572. Electronic Modeling. 2018. V. 40. ¹ 1
38. McCarty, L.T. (1995), An implementation of Eisner vs Macomber, Proceedings of the Fifth
International Conference on AI and Law, ACM Press, New York, USA, pp. 276-286, doi:
HYPERLINK «https://doi.org/10.1145/222092.222258" \t »_self" 10.1145/222092.222258.
39. Moles, R.N. and Dayal, S. (1992), There is more to life than logic, Journal of Information
Science, Vol. 3, no. 2, pp.188-218.
40. Wyner, A. (2008), An ontology in OWL for legal case-based reasoning, Artificial Intelli-
gence and Law, Vol. 16, pp. 271-283, doi: 10.1007/s10506-008-9070-8.
41. Prakken, H. (2006), Artificial intelligence and law, logic and argument schemes, Arguing on
the Toulmin Model, Eds D. Hitchcock and B. Verheij, Dordrecht Springer, Berlin, Germany,
pp. 91-117, doi: 10.1007/978-1-4020-4938-5_15.
42. Ashley, K. and Bruninghaus, S. (2009), Automatically classifying case texts and predicting
outcomes, Artificial Intelligence and Law, Vol. 17, no. 2, pp. 125-165, doi: 10.1007/s10506-
009-9077-9/
Received 27.10.17
Ñ.À. Êîñåíêî
ÎÑÍÎÂÍÛÅ ÏÎËÎÆÅÍÈß ÒÅÎÐÈÈ ÎÍÒÎËÎÃÈÉ
È ÅÅ ÂÍÅÄÐÅÍÈÅ Â ÑÈÑÒÅÌÓ ÏÐÀÂÎÂÛÕ ÇÍÀÍÈÉ
Ïðåäñòàâëåíû îáîáùåííûå äàííûå î ïðîèñõîæäåíèè ïîíÿòèÿ «îíòîëîãèÿ» è ïðîàíàëèçè-
ðîâàíû ïóòè åãî äàëüíåéøåé òðàíñôîðìàöèè äëÿ ïðèìåíåíèÿ â ñèñòåìàõ èñêóññòâåííîãî
èíòåëëåêòà, ãäå îíòîëîãèÿ ïîíèìàåòñÿ êàê êîìïëåêñ çíàíèé, ïðåäñòàâëÿþùèõ îïðå-
äåëåííóþ èíôîðìàöèþ îá îáúåêòå èññëåäîâàíèÿ. Â íàñòîÿùåå âðåìÿ ðàçðàáîòàí ðÿä
ðàçëè÷íûõ îíòîëîãèé, â ÷àñòíîñòè ïîâåðõíîñòíûå, òîïîâûå, äîìåííûå è äðóãèå, êîòî-
ðûå ÿâëÿþòñÿ îñíîâîé ïðè ðàçðàáîòêå ñèñòåìû èñêóññòâåííîãî èíòåëëåêòà ñ èñïîëü-
çîâàíèåì íàêîïëåííûõ çíàíèé è áàç äàííûõ äëÿ óñîâåðøåíñòâîâàíèÿ ïðîöåññà ëî-
ãè÷åñêîãî ìûøëåíèÿ è ïðèíÿòèÿ ñîîòâåòñòâóþùèõ ðåøåíèé. Îñîáîå çíà÷åíèå èìåþò
îíòîëîãèè â ïðàâîâåäåíèè äëÿ ôîðìàëèçàöèè çàêîíîâ, ïðèíÿòèÿ ñóäåáíûõ ðåøåíèé è
ïîäà÷è èíôîðìàöèè îá îïðåäåëåííûõ ïðåöåäåíòàõ è íåòèïè÷íûõ ñëó÷àÿõ. Îïèñàíû
êðèòåðèè äèçàéíà îíòîëîãèé, à òàêæå îñîáåííîñòè èõ ïðèìåíåíèÿ â ïðàâîâîì äîìåíå.
Ôîðìèðîâàíèå îíòîëîãèé èìååò ñïåöèôè÷åñêèå çàäà÷è, íî îòñóòñòâóþò êàêèå-ëèáî ñïî-
ñîáû ôîðìèðîâàíèÿ èõ ñîäåðæàíèÿ è äèçàéíà. Îñíîâíàÿ çàäà÷à, êîòîðàÿ äîëæíà áûòü
âûïîëíåíà ïðè ñîçäàíèè îíòîëîãèè, ñâÿçàíà ñî ñòðîãèì è ÷åòêèì ôîðìóëèðîâàíèåì
ñóòè ïîíÿòèÿ «îíòîëîãèÿ», ó÷èòûâàÿ ñâÿçü ìåæäó ðàçëè÷íûìè îíòîëîãèÿìè.
Ê ë þ ÷ å â û å ñ ë î â à: îíòîëîãèÿ, ïðàâî, èñêóññòâåííûé èíòåëëåêò, êîíöåïòóàëèçàöèÿ,
ïðàâîâîé äîìåí.
KOSENKO Serhii Oleksandrovich, post-graduate student of the Georgy Pukhov Institute for Energy
Modelling of NAS of Ukraine; graduated from Yuriy Fedkovych Chernivtsi National University in
1997. The field of research: artificial intelligence.
The Main Statements of Ontology Theory and Its Implementation
ISSN 0204–3572. Åëåêòðîí. ìîäåëþâàííÿ. 2018. Ò. 40. ¹ 1 113
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