Article

Article title METHOD OF CREATING A DOMAIN ONTOLOGY FROM GLOSSARY
Authors Yu.A. Kravchenko, A.A. Novikov, V.V. Markov
Section SECTION IV. ARTIFICIAL INTELLIGENCE AND FUZZY SYSTEMS
Month, Year 06, 2015 @en
Index UDC 002.53:004.89
DOI
Abstract The article describes the development of a method to create the ontology of one or more glossaries. A feature of this method is the parallel construction of the ontology and the formation of the domain full terms. The method allows to identify the main stages of building ontology-based iterative reflection concepts glossary in the objects of the ontology. This process is automated, but in the early stages of building it must be adjusted manually using the ontology developers experts in the subject area. In step of clustering the glossary terms is grouped into clusters based on the algorithm of k-means clustering. The set of glossary elements is divided into a known k number of clusters. The algorithm finishes its work, when the clusters do not change. Then the addition of new (not described in the glossary) of terms and their definitions. For each clusters is define the relation between all objects in cluster. It provides an informal description of the values and characteristics for each relation. Then we get a conceptual model of ontology, which is a generalization of the concepts of data. The result is a display of the resulting ontology in a graphical form by using different ontologies editors e.g. Ontolingua, OntoEdit, OilEd, WebOnto, ODE, Protégé. The final ontology reflects the concepts, with indicating a precise definition and ontology is completed with respect to the specific subject area.

Download PDF

Keywords Domain ontology; clustering; classification; knowledge representation system.
References 1. Gruber T.R. A Translation Approach to Portable Ontology Specifications, Knowledge Acquisition, 1993, No. 5 (2), pp. 199-220.
2. Kravchenko Yu.A., Zaporozhets D.Yu., Lezhebokov A.A. Sposoby intellektual'nogo analiza dannykh v slozhnykh sistemakh [Methods data mining in complex systems], Izvestiya KBNTs RAN [Izvestiya of Kabardino-Balkar scientific centre of the RAS], 2012, No. 3 (47), pp. 52-57.
3. Tuzovskiy A.F., Chirikov S.V., Yampol'skiy V.Z. Sistemy upravleniya znaniyami (metody i tekhnologii) [The knowledge management system (methods and technologies)]. Under the General ed. V.Z. Yampol'skogo. Tomsk: Izd-vo NTL, 2005, 260 p.
4. Kravchenko Yu.A., Kureichik V.V. Knowledge management based on multi-agent simulation in informational systems, Conference proceedings. 8th IEEE International Conference “Application of Information and Communication Technologies – AICT 2014”. 15-17 October 2014, Astana, Kazakhstan, pp. 264-267.
5. Bashmakov A.I., Bashmakov I.A. Intellektual'nye informatsionnye tekhnologii: Ucheb. Posobie [Intelligent information technologies: a tutorial]. Moscow: MGTU im. N.E. Baumana, 2005, 304 p.
6. Kureichik V.M., Rodzin S.I. Evolutionary algorithms: genetic programming, Journal of Computer and Systems Sciences International, 2002, Vol. 41, No. 1, pp. 123-132.
7. Kureychik V.V., Sorokoletov P.V. Kontseptual'naya model' predstavleniya resheniy v geneticheskikh algoritmakh [A conceptual model of representation solutions in genetic algorithms], Izvestiya YuFU. Tekhnicheskie nauki [Izvestiya SFedU. Engineering Sciences], 2008, No. 9 (86), pp. 7-12.
8. Intellektual'nyy analiz dannykh. Algoritm klasterizatsii k-means [Data mining. The clustering algorithm k-means]. Available at: http://intellect-tver.ru/?p=265 (Accessed 14 May 2015).
9. Kureychik V.M. Osobennosti postroeniya sistem podderzhki prinyatiya resheniy [Features of decision making support system design], Izvestiya YuFU. Tekhnicheskie nauki [Izvestiya SFedU. Engineering Sciences], 2012, No. 7 (132), pp. 92-98.
10. Kravchenko Yu.A., Bova V.V. Nechetkoe modelirovanie raznorodnykh znaniy v intellektual'nykh obuchayushchikh sistemakh [Fuzzy modeling heterogeneous knowledge in intelligent tutoring systems], Otkrytoe obrazovanie [Open Education], 2013, No. 4 (99), pp. 70-74.
11. Iskusstvennyy intellekt. Metody klassifikatsii [Artificial intelligence. Classification methods]. Available at: http://www.aiportal.ru/articles/autoclassification/methods-class.html (Accessed 14 May 2015).
12. Kureychik V.M. Osobennosti postroeniya sistem podderzhki prinyatiya resheniy [Features of decision making support system design], Izvestiya YuFU. Tekhnicheskie nauki [Izvestiya SFedU. Engineering Sciences], 2012, No. 7 (132), pp. 92-98.
13. Bova V.V., Kureychik V.V., Nuzhnov E.V. Problemy predstavleniya znaniy v in-tegrirovannykh sistemakh podderzhki upravlencheskikh resheniy [Problems of knowledge presentation in management decision support of integrated systems], Izvestiya YuFU. Tekhnicheskie nauki [Izvestiya SFedU. Engineering Sciences], 2010, No. 7 (108), pp. 107-113.
14. Kureychik V.V., Rodzin S.I. O pravilakh predstavleniya resheniy v evolyutsionnykh algoritmakh [On the rules for the submission decisions in evolutionary algorithm], Izvestiya YuFU. Tekhnicheskie nauki [Izvestiya SFedU. Engineering Sciences], 2010, No. 7 (108), pp. 13-21.
15. OntoEdit: Collaborative ontology development for the Semantic Web. Y. Sure, M. Erdmann, J. Angele, S. Staab, R. Studer, D. Wenke, In Proc. of the Inter. Semantic Web Conference (ISWC 2002), Sardinia, Italia, June 2002.
16. ODE, WebODE. Available at: http://www.delicias.dia.fi.upm.es/webODE/ (Accessed 14 May 2015).
17. Ontolingua. Available at: http://www.ksl.stanford.edu/software/ontolingua/ (Accessed 14 May 2015).
18. Protege. Available at: http://www.protege.stanford.edu/download/registered.html (Accessed 14
May 2015).
19. WebOnto. Available at: http://webonto.open.ac.uk (Accessed 14.05.2015).
20. Noy N., Musen M. The PROMPT Suite: Interactive Tools For Ontology Merging And Mapping, Stanford Medical Informatics, Stanford Univ.

Comments are closed.