A methodology for ontological knowledge capture from databases
The successful emergence of the Information and Communication Technologies (ICT) has contributed to the efficiency improvement in a number of economic sectors. However, some strategic economic sectors, such as construction, have not been targeted enough yet. Construction-related ICT solutions lack m...
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Інститут програмних систем НАН України
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| Цитувати: | A methodology for ontological knowledge capture from databases / F. García-Sánchez, J.T. Fernández-Breis, R. Martínez-Bejar // Пробл. програмув. — 2008. — N 2-3. — С. 431-437. — Бібліогр.: 9 назв. — англ. |
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Digital Library of Periodicals of National Academy of Sciences of Ukraine| _version_ | 1859617162811932672 |
|---|---|
| author | García-Sánchez, F. Fernández-Breis, J.T. Martínez-Bejar, R. |
| author_facet | García-Sánchez, F. Fernández-Breis, J.T. Martínez-Bejar, R. |
| citation_txt | A methodology for ontological knowledge capture from databases / F. García-Sánchez, J.T. Fernández-Breis, R. Martínez-Bejar // Пробл. програмув. — 2008. — N 2-3. — С. 431-437. — Бібліогр.: 9 назв. — англ. |
| collection | DSpace DC |
| description | The successful emergence of the Information and Communication Technologies (ICT) has contributed to the efficiency improvement in a number of economic sectors. However, some strategic economic sectors, such as construction, have not been targeted enough yet. Construction-related ICT solutions lack mechanisms to permit the effective integration of the whole supply chain. Semantic Web can tackle these issues. This paper presents a methodology for acquiring knowledge from construction-related databases. A domain ontology has been developed that contains the relevant concepts regarding supply management in the construction domain. The methodology basically consists of mapping the database content onto the ontology and a further this one’s population by applying a set of mapping rules.
Успешное появление информационно-коммуникационных технологий (ИКТ) внесло свой вклад в повышение эффективности многих секторов экономики. Однако, некоторые стратегические экономические сектора, такие, как строительство, не были все же достаточно исследованы. Связанные со строительством решения ИКТ испытывают недостаток в механизмах, позволяющих разрешать проблемы эффективной интеграции полной цепочки поставки. Семантическая Сеть может заняться этими проблемами. Эта статья представляет методологию, позволяющую извлекать знание из баз данных, связанных со строительством . Была разработана онтология домена, содержащая релевантные понятия, касающиеся управления поставками в домене строительства. Методология в основном состоит из отображения содержания базы данных на онтологию и дальнейшего ее заполнения , применяя набор правил отображения .
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| first_indexed | 2025-11-28T20:14:29Z |
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Моделі і засоби систем баз даних і знань
© F. García-Sánchez, J.T. Fernández-Breis, R. Martínez-Bejar, 2008
ISSN 1727-4907. Проблеми програмування. 2008. № 2-3. Спеціальний випуск 431
UDC: 004.8, 005.94
A METHODOLOGY FOR ONTOLOGICAL KNOWLEDGE
CAPTURE FROM DATABASES
F. García-Sánchez, J.T. Fernández-Breis, R. Martínez-Bejar
Department of Computing and Systems, Faculty of Computer Science,
University of Murcia, Facultad de Informática, Campus de Espinardo,
30100 Espinardo (Murcia), Spain, +34 968 39 8107, frgarcia@um.es,
+34 968 36 4613, jfernand@um.es,
+34 968 36 4634, rodrigo@um.es
The successful emergence of the Information and Communication Technologies (ICT) has contributed to the efficiency improvement in a
number of economic sectors. However, some strategic economic sectors, such as construction, have not been targeted enough yet. Construction-
related ICT solutions lack mechanisms to permit the effective integration of the whole supply chain. Semantic Web can tackle these issues. This
paper presents a methodology for acquiring knowledge from construction-related databases. A domain ontology has been developed that
contains the relevant concepts regarding supply management in the construction domain. The methodology basically consists of mapping the
database content onto the ontology and a further this one’s population by applying a set of mapping rules.
Успешное появление информационно-коммуникационных технологий (ИКТ) внесло свой вклад в повышение эффективности многих
секторов экономики. Однако, некоторые стратегические экономические сектора, такие, как строительство, не были все же достаточно
исследованы. Связанные со строительством решения ИКТ испытывают недостаток в механизмах, позволяющих разрешать проблемы
эффективной интеграции полной цепочки поставки. Семантическая Сеть может заняться этими проблемами. Эта статья представляет
методологию, позволяющую извлекать знание из баз данных, связанных со строительством . Была разработана онтология домена,
содержащая релевантные понятия, касающиеся управления поставками в домене строительства. Методология в основном состоит из
отображения содержания базы данных на онтологию и дальнейшего ее заполнения , применяя набор правил отображения .
Introduction
The construction sector requires the effective management of large volume of data, information about building site
processes, provider data, and so on. Most of the cost in this sector focuses on supplies and manpower, whose efficient
management would produce both money and time savings. Besides, properties prices would decrease in the medium
term. Each building is different and it should be treated as such. Customers should be able to get properties in
accordance to their specific preferences. However, there are different elements in each building, contrasting ideas
between customers and builders. In addition to this, the information regarding a particular building site can be so wide
that a classification mechanism becomes necessary in order to take advantage of it. Furthermore, builders and customers
must deal with supplies from different suppliers, and each supplier has its own information system and way of
structuring the information concerning its supplies. This situation also happens with suppliers of the same type of
product. Hence, mechanisms for harmonizing this heterogeneity should be pursued in order to facilitate the labour of
both builders and customers.
Additionally, part of the knowledge and experience acquired from the development of a new building is currently
kept by the personnel who have worked in that building site. Only in case the very same personnel works in another
building site this knowledge and experience could be reused, otherwise it would be lost. Thus, if the knowledge
acquired by the personnel is stored and matched against the potential customers’ knowledge, a better supply schedule
could be performed either automatically or semi-automatically (i.e. supervised). Furthermore, if that information is
shareable and reusable by different members of the staff in the same company, the management and control of the
supply material could be done more efficiently within the company.
Traditionally, adaptors and exchange formats have been applied to promote interoperability between information
systems, without significant success yet. To face this problem, alternative approaches have been proposed that make use
of semantic technologies to facilitate integration and interoperability [1]. An advantage of using semantic approaches is
the fact that they do not require to replace current integration technologies, databases and applications. Moreover, they
add a new layer that takes advantage of the already existing infrastructure [2]. Semantic Web technologies [3] are useful
for our purpose. Amongst the core Semantic Web technologies, ontologies are basic to promote semantic
interoperability between independent and heterogeneous systems such as the World Wide Web. Modelling the
information by means of ontologies leads to an environment where builders can be aware of all the information
regarding a building site at any time. Ontologies permit shareable, reusable, and machine-readable modeling of
information, so most of the tasks regarding that information management information can be automated. Thereby, the
organization increases its processes efficiency and has all the relevant elements needed to make an optimal control of
the supplies integrated.
Моделі і засоби систем баз даних і знань
432
In practical settings, ontologies are more and more used in information management due to the advantages they
have. On the one hand, ontologies are reusable, that is, a same ontology can be reused in different applications, either
individually or in combination with other ontologies. On the other hand, ontologies are shareable, that is, their
knowledge allows for being shared by a particular community.
The main goal of the approach presented here is to allow for the creation, integration and management of supply
information in the construction domain. Such approach is based on Knowledge Management and Semantic Web
technologies. Basically, the information that is obtained from databases and heterogeneous sources is modelled by
means of knowledge management systems. After that, different tools can be ideated in order to allow an optimal access
and management to the relevant supply information in the construction domain.
The rest of the paper is organized as follows. Section 2 offers an overview on the technologies applied in this
approach. In Section 3, the methodology for knowledge acquisition from databases in construction is described and an
example is depicted in Section 4 in order to show how the methodology works. Finally, in Section 5 some conclusions
and further work plans are presented.
1. Methodological Foundations
The approach presented here aims at putting together different technologies related to the Semantic Web, such as
ontologies (due to their adequacy in solving integration and interoperability problems) and Schema Integration. This
section presents a brief overview of these technologies and how the proposed solution benefits from their use.
1.1 Ontologies. One of the most widespread definitions is ontology is Tom Gruber's [4]: "An ontology is an
explicit specification of a conceptualization”. An ontology represents a common, shareable and reusable view of a
particular application domain. Moreover, ontologies are used to give meaning to information structures that are
exchanged by information systems. An ontology is essentially a formal and structure information conceptual model. An
ontology is here seen as a semantic model containing concepts, their properties, interconceptual relations, and axioms
related to the previous elements. In this work, one of the objectives is to organize and model information about the
construction domain into ontologies. For it, all taxonomies (e.g. there are different classes of bricks, tiles, slabs, etc),
partonomies (e.g. a brick is part of a wall, the kitchen is part of a house), and chronologies (e.g. you have to paint after
every wall and the roof have been pargeted) can be defined. In this work, the ontological content is expressed by using
the Ontology Web Language (OWL), which is the W3C recommendation for exchange of ontologies on the Web (Web
Ontology Working Group, 2004).
1.2 Ontologies for Integration and Interoperability. Nowadays, databases contain a huge amount of data.
However, the integration of different databases in order to provide a uniform access to them has not been fully provided
yet. Data integration requires real-time transformations of the information that flows between systems. The
transformations must take into account the semantic differences between the applications. The most important factors
that make it difficult to integrate and obtain interoperability between systems are the semantic and structural
heterogeneity, as well as the different meaning assigned to information by different systems. In this context, ontologies
facilitate the human understanding of the information besides the information-based access and the information
integration from very different information systems. Ontologies allow for differentiating among resources, and this is
especially useful when there are resources with redundant data. Thus, they help to fully understand the meaning and
context of information. This is important for our objective of achieving semantic interoperability among different
resources. Ontologies have been already used for the integration of databases in order to provide interoperability among
different information systems in different domains such as biology and medicine. Examples can be found in [5], where
ontologies were used to promote integration and interoperability between information systems for three medical
communities by combining data with HL7 and terminologies such as UMLS, MEDCIN and SMOMED, or [6] where
they are used to promote interoperability among electronic healthcare records information models.
1.3 Schema Integration. Another approach related to this work is that of Schema Integration. It is defined as “the
process of generating one or more integrated schemas from existing schemas” [7]. The goal of the schema integration
methods is to allow applications to transparently view and query data from multiple data sources as if they were one
uniform data source. The idea in schema integration is to use mapping rules to handle the structural differences between
the different data sources.
In [8] the authors present a four-phase integration process. The first phase is called ‘Preintegration’, moment in
which database administrators and designers select schemas, decide the order of integration and set an integration policy
or preference. During the second phase the schemas are analyzed and compared to detect possible schema and data
conflicts. In the third phase, the requirements and conflicts for the merging are identified, requiring a close interaction
between designers and users. Finally, the actual schema combination is performed. The blackboard architecture has
been also use for schema integration [9]. Using the blackboard architecture, multiple knowledge agents were able to
cooperate in spite of accessing different disparate knowledge sources.
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2. A Methodology for the Semantic Management of Construction Supplies
The methodology presented in this work consists of four steps. The final aim of the methodology is to acquire
knowledge (in the form of an instantiated ontology) from relational databases in the construction sector. It focuses on
the construction domain but could be generalized to any other domain that shares some of the properties of the
construction sector such as its stability (i.e. new elements that modify the domain model do not usually appear over
time).
− Step 1: Build a general domain ontology scheme. During this step, the ontology scheme is developed. For this task,
in-depth knowledge of the domain is required. The construction of this ontology is critical as it has an influence on
the rest of the process. Thus, an expert is responsible for manually doing this step. Once the scheme is complete it
can be extended according to changes in the domain.
Repeat for each database…
− Step 2: Get the map between the ontology and a relational database. In order to be able to instantiate the different
concepts of the ontology resulting from carrying out Step 1 with the contents of the databases, a mapping between
them is needed. The mapping process is manually done. Each element in the database scheme (i.e. all the database
columns) is to be matched, on a one-by-one basis, against an element of the ontology (i.e., a concept, a property or an
interconceptual relation). This step may also give rise to refinements in the ontology.
− Step 3: Populate the ontology. The third step of the methodology concerns the process of populating the ontology.
Now, using the mapping rules previously obtained, the information contained in the database is mapped onto its
correspondent element of the ontology. During this process, which is automatically performed, new instances of
ontological elements are created along with the association between the attributes (i.e. concept properties) and their
values.
End Repeat.
− Step 4: Ontology evolution. The general ontology schema should evolve according to the changes produced in the
world (requirements, source databases, etc). Therefore, a continuous checking process needs to be performed to
assure the consistency.
At this moment in time, most of the steps that comprise the methodology have to be performed manually. However,
in the near future it is expected that research on different application fields will lead to a fully automated process.
3. Example
In this section an application of the methodology under question is illustrated by means of an example. This
example use represents a typical problem in trying to integrate the access to two different databases, so that user queries
are uniform. In particular, two different relational databases referring to the same real world elements but with different
data schemes are integrated by means of a common general ontology model.
The first step, as indicated in the methodology, is to get a general domain ontology scheme. It has to be done once for
each different application domain. As we are dealing with the construction industry, the ontology scheme to develop
should show the concepts and specific features of this domain. Several different ontology models could be constructed
depending on the modeller’s point of view or the concrete properties of the problem to be solved. In Fig. 1, an extract of
the domain ontology is depicted. The OWL file of the whole ontology can be found at
http://klt.inf.um.es/ontologies/ConstructionSuppliesManagement.owl.
Fig. 1. Domain ontology
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434
Four main concepts can be highlighted: the economic activity which a company is engaged in, the organization that
needs or can provide a supply, the order a company makes to another related to a particular construction material, and
the actual construction supply ordered. Apart from both customers and suppliers, two taxonomies have been designed,
the economic activities taxonomy, and the building and construction taxonomy. In this figure, different interconceptual
relations are depicted. For example, each organization performs its activity in a particular sector or economic activity,
or the consumer organization (i.e. customer) makes an order of a supply to a provider organization (i.e. supplier). The
economic activities taxonomy is based on the “International Standard Industrial Classification of all Economic
Activities, Revision 3.1” that is maintained by the United Nation Statistics Division, Statistical Classification Section
(http://unstats.un.org/unsd/cr/family2.asp?Cl=17).
The construction supplies taxonomy is based on a taxonomy previously developed by WAND Inc.
(www.wandinc.com) and found through the Taxonomy Warehouse (www.taxonomywarehouse.com).
For the next step, according to the methodology described, the databases need to be identified. In this example, we
are dealing with simple data schemes as shown in the following figures (Fig. 2 and Fig. 3). The schemes represent two
different models related to construction supply transactions. Scheme A can be the one used by a supplier company
while scheme B can be the one used by the builder company that needs supplies. Although they are different, they aim
at modelling the same domain elements.
Fig. 2. Database Scheme A
Fig. 3. Database Scheme B
The entity-relation diagram in Fig. 2 presents three different entities (Company, Order, and Good) and two
relations. The database itself would consist of three tables, each including a primary key (ID for every table) and table
Order holding two foreign keys (Company_ID and Good_ID). The entity-relation diagram in Fig. 3 presents a similar
scheme to that in Fig. 2 but with a number of differences. On the one hand, supplies are modelled by using different
relational entities. Model A consists of three entities, one for companies, one for goods and one for orders, whereas
Model B represents supplies orders with one single entity, SupplyOrder.
Besides, several attributes have changed in different ways. For example, Quantity in scheme A is termed Amount in
scheme B. On the other hand, attribute Address in scheme A has been split up into four different attributes in scheme B,
namely Street, Number, City, and Country. These differences would make it harder for companies to communicate with
business partners. Once the databases have been identified, the manual mapping process starts. As it was explained
above, this step is to be done manually because no satisfactory automatic solution to this problem has been obtained yet.
For each database scheme, a separate mapping should be defined. Moreover, for each element in the database scheme a
unique relation with an element of the ontology has to be identified. The mappings found between the ontology model
and both database models are graphically represented in Fig. 4. It is worthy to explain some of the decisions taken when
elaborating the mapping rules. For example, composed attributes in the databases, such as Address and Date in database
A, are mapped onto ontology concepts. Therefore, a method for splitting up both attribute values and fulfilling the
concepts properties has been applied. On the other hand, database foreign keys are mapped not to concept attributes but
to interconceptual relationships. Thus, for example, attribute Company_ID in database A corresponds with the relation
between concept Order and concept Organization in the ontology.
Моделі і засоби систем баз даних і знань
435
Fig. 4. Mapping rules
The final step in the methodology refers to the process of automatically populating the ontology. In order to do this,
it is needed to take into account the mappings rules previously defined. From each attribute of each row in the database
tables new instances in the ontology emerge and are fulfilled. In the following tables, a few of the rows in the tables are
shown.
In Table 1, a few rows of table Company in database A are depicted. This table contains two different rows for two
different companies and their data.
Table 1. Table ‘Company’; Database A
ID Name Address Telephone
1 Company A Address A +34 111 111111
2 Company B Address B +34 222 222222
In Table 2, two types of supplies are presented and a brief description of each is given. They belong to table Good
in database A.
Table 2. Table ‘Good’; Database A
ID Type Subtype Description
1 Construction
material
Brick Clay bricks. The dimensions
are 230 x 110 x 76 mm
2 Lighting Bulb E14 / E27 screw fittings, used
in continental Europe. 100 W,
1700 lumens
In Table 3 the last table pertaining to database A is presented, the table Order. Two rows of this table are shown.
Table 3. Table ‘Order’; Database A
ID Company_I
D
Good_I
D
Quantity Price Date
1 1 2 500 1000 02/05/200
5
2 2 1 1000 2000 10/12/200
5
Моделі і засоби систем баз даних і знань
436
In Table 4, the entity Organization of database B is depicted. The data regarding two different rows are revealed.
Table 4. Table ‘Organization’; Database B
ID CorporateNa
me
Street Numbe
r
City Country Telephone
1 Company C St C 45 Madri
d
Spain +34 111
111111
2 Company D St D 21 Galwa
y
Ireland +34 222
222222
In Table 5, two supply orders are highlighted. They belong to table SupplyOrder in database B.
From these data stored in the different databases, a joint set of ontology instances emerges. In Fig. 5, some of the
instances obtained from these heterogeneous data sources are depicted. They have been generated using Ontoviz plug-in
for Protégé. Thus, the feasibility of offering a common view from heterogeneous data sources is proven. It should be
noted that real names of the companies have been intentionally replaced.
Table 5. Table 'SupplyOrder'; Database B
ID Amount Price Day .. .. SupplyTyp
e
.. OrganizationI
D
1 20 500 25 .. .. Door .. 2
2 2000 4 10 .. .. Lighting .. 1
Fig. 5. Ontology instances
4. Conclusions and Future Work
The emergence of the Semantic Web has led to the establishment of ontologies as the de facto standard for
knowledge representation. Ontologies permit shareable, reusable, and machine-readable modeling of information, so
most of the task regarding the management of the information can be automated. The use of ontologies has then been
widespread across many economic sectors. However, construction and building sector has not been targeted enough yet
and there is no effective approach to allow for effective data exchange between business partners in this domain.
Besides, a more sophisticated solution would permit a better understanding of company’s own processes and an
improved supply management.
In this paper, a methodology for knowledge acquisition from databases in the construction domain is presented.
The methodology consists of a set of simple steps that leads to the elaboration and instantiation of a common and shared
ontology. This ontology usefulness is twofold: it facilitates a proper control over data and a better understanding of
supply statuses, and it allows for effective data exchange between builders and their suppliers. This methodology is
based on previous research studies on knowledge discovery and knowledge discovery from databases, as well as on
ontology learning and data integration. We claim that by applying ontology learning techniques and ontologies as
knowledge representation, results in knowledge discovery from databases can be improved.
This constitutes a solution to a major issue in companies’ relationships, intercommunication. In the building and
construction industry, a common issue to solve appears when both the supplier and the builder do not share a common
data model. By sharing a common ontology in an upper level of abstraction, instead of exchanging messages containing
elements of the database, both the supplier and the builder use terms of the ontology to intercommunicate. The ultimate
goal of the undergoing research presented here is to build a platform for supply information creation, integration, and
Моделі і засоби систем баз даних і знань
437
management in the building and construction domain based on knowledge management technologies and the Semantic
Web. This integrated platform would make it possible for users to easily access to the knowledge acquired from
heterogeneous information sources and databases. For this to be successfully accomplished, several milestones should
be satisfied. The first step is the development of a Web application for cooperative and automatic building of
construction supply ontologies. Then, a user-friendly interface need to be designed in order to enable final users (i.e.
builders) to access in an intelligent manner to the supply information/knowledge stored. Finally, as knowledge
acquisition is an incremental process, (semi)automatic mechanisms for knowledge refinement should be ideated.
However, a number of challenges should be faced yet. Some of these challenges are, for example, the construction of
large, useful ontologies that are shared by many, and the (semi)automatic creation of mappings.
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| id | nasplib_isofts_kiev_ua-123456789-1510 |
| institution | Digital Library of Periodicals of National Academy of Sciences of Ukraine |
| issn | 1727-4907 |
| language | English |
| last_indexed | 2025-11-28T20:14:29Z |
| publishDate | 2008 |
| publisher | Інститут програмних систем НАН України |
| record_format | dspace |
| spelling | García-Sánchez, F. Fernández-Breis, J.T. Martínez-Bejar, R. 2008-07-31T16:01:17Z 2008-07-31T16:01:17Z 2008 A methodology for ontological knowledge capture from databases / F. García-Sánchez, J.T. Fernández-Breis, R. Martínez-Bejar // Пробл. програмув. — 2008. — N 2-3. — С. 431-437. — Бібліогр.: 9 назв. — англ. 1727-4907 https://nasplib.isofts.kiev.ua/handle/123456789/1510 004.8, 005.94 The successful emergence of the Information and Communication Technologies (ICT) has contributed to the efficiency improvement in a number of economic sectors. However, some strategic economic sectors, such as construction, have not been targeted enough yet. Construction-related ICT solutions lack mechanisms to permit the effective integration of the whole supply chain. Semantic Web can tackle these issues. This paper presents a methodology for acquiring knowledge from construction-related databases. A domain ontology has been developed that contains the relevant concepts regarding supply management in the construction domain. The methodology basically consists of mapping the database content onto the ontology and a further this one’s population by applying a set of mapping rules. Успешное появление информационно-коммуникационных технологий (ИКТ) внесло свой вклад в повышение эффективности многих секторов экономики. Однако, некоторые стратегические экономические сектора, такие, как строительство, не были все же достаточно исследованы. Связанные со строительством решения ИКТ испытывают недостаток в механизмах, позволяющих разрешать проблемы эффективной интеграции полной цепочки поставки. Семантическая Сеть может заняться этими проблемами. Эта статья представляет методологию, позволяющую извлекать знание из баз данных, связанных со строительством . Была разработана онтология домена, содержащая релевантные понятия, касающиеся управления поставками в домене строительства. Методология в основном состоит из отображения содержания базы данных на онтологию и дальнейшего ее заполнения , применяя набор правил отображения . en Інститут програмних систем НАН України Моделі і засоби систем баз даних і знань A methodology for ontological knowledge capture from databases Article published earlier |
| spellingShingle | A methodology for ontological knowledge capture from databases García-Sánchez, F. Fernández-Breis, J.T. Martínez-Bejar, R. Моделі і засоби систем баз даних і знань |
| title | A methodology for ontological knowledge capture from databases |
| title_full | A methodology for ontological knowledge capture from databases |
| title_fullStr | A methodology for ontological knowledge capture from databases |
| title_full_unstemmed | A methodology for ontological knowledge capture from databases |
| title_short | A methodology for ontological knowledge capture from databases |
| title_sort | methodology for ontological knowledge capture from databases |
| topic | Моделі і засоби систем баз даних і знань |
| topic_facet | Моделі і засоби систем баз даних і знань |
| url | https://nasplib.isofts.kiev.ua/handle/123456789/1510 |
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