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dc.contributor.authorLytvyn, Vasyl-
dc.contributor.authorVysotska, Victoria-
dc.contributor.authorWojcik, Waldemar-
dc.contributor.authorDosyn, Dmytro-
dc.date.accessioned2018-02-22T11:38:56Z-
dc.date.available2018-02-22T11:38:56Z-
dc.date.issued2017-
dc.identifier.citationA method of construction of automated basic ontology / Vasyl Lytvyn, Victoria Vysotska, Waldemar Wojcik, Dmytro Dosyn // Computational linguistics andintelligent systems (COLINS 2017) : proceedings of the 1st International conference, Kharkiv, Ukraine, 21 April 2017 / National Technical University «KhPI», Lviv Polytechnic National University. – Kharkiv, 2017. – P. 75–83. – Bibliography: 17 titles.uk_UA
dc.identifier.urihttps://ena.lpnu.ua/handle/ntb/39460-
dc.description.abstractThe paper describes an approach to development of a computer system that automatically constructs an ontology base. Basic modules of the system and its operation are described, as well as the choice of software tools for implementation. Application of the proposed system allows to fill the domain ontology in an automatic mode. Therefore, this paper introduces an approach to development of an automated basic ontology composition. An architecture of synthesis of the ontology system is created using CROCUS (Cognition Relations or Concepts Using Semantics) software model. The main system modules and their functions are described. A decision of SDK for system realization is justified. Application of the proposed system can fill an ontology of subject area automatically.uk_UA
dc.language.isoenuk_UA
dc.publisherNational Technical University «KhPI»uk_UA
dc.subjectcomputer systemuk_UA
dc.subjectontologyuk_UA
dc.subjectknowledge baseuk_UA
dc.subjectdatabaseuk_UA
dc.subjecttext documentuk_UA
dc.subjectmachine learninguk_UA
dc.subjectintelligent agentuk_UA
dc.subjectutilityuk_UA
dc.subjectsemanticuk_UA
dc.subjectlogic of predicateuk_UA
dc.titleA method of construction of automated basic ontologyuk_UA
dc.typeConference Abstractuk_UA
dc.contributor.affiliationLviv Polytechnic National Universityuk_UA
dc.contributor.affiliationInstitute of Electronics and Information Technology, Lublin University of Technologyuk_UA
dc.contributor.affiliationSystems Analysis Laboratory, Karpenko Physico-Mechanical Institute of the NAS of Ukraineuk_UA
dc.coverage.countryUAuk_UA
dc.format.pages75-83-
dc.relation.referencesen1. Ourania Hatzi, Dimitris Vrakas, Nick Bassiliades, (2010). Dimosthenis Anagnostopoulos, and Ioannis Vlahavas. The PORSCE II Framework: Using AI Planning for Automated Semantic Web Service Composition the Knowledge Engineering Review, Cambridge University Press, Vol. 02:3, 1–24 p. (In English) 2. Link Grammar – Carnegie Mellon University, available at: http://bobo.link.cs.cmu.edu/link. 3. Qiu Ji, Peter Haase, and Guilin Qi (2008). Combination of Similarity Measures in Ontology Matching using the OWA Operator, In Proceedings of the 12th International Conference on Information Processing and Management of Uncertainty in Knowledge- Base Systems. 4. Lytvyn V. (2013). Design of intelligent decision support systems using ontological approach, An international quarterly journal on economics in technology, new technologies and modelling processes, Krakiv-Lviv, Vol. II, No 1, 31 – 38 (In English). 5. Gruber T. A. (1993). Translation approach to portable ontologies. Knowledge Acquisition, № 5 (2):199–220. 6. Guarino N. (1995). Formal Ontology, Conceptual Analysis and Knowledge Representation. International Journal of Human-Computer Studies, 43(5-6):625–640. 7. Sowa J. (1992). Conceptual Graphs as a universal knowledge representation. In: Semantic Networks in Artificial Intelligence, Spec. Issue of An International Journal Computers & Mathematics with Applications. (Ed. F. Lehmann), № 2–5:75–95. 8. Montes-y-Gómez M. (2000). Comparison of Conceptual Graphs [Електронний ресурс]. Lecture Notes in Artificial Intelligence, Vol. 1793. – Springer-Verlag. Режим доступу до журналу: http://ccc.inaoep.mx/~mmontesg/publicaciones/ 2000/ComparisonCG. 9. Muller H.M., Kenny E.E., Sternberg P.W. (2004). ―An Ontology-Based Information Retrieval and Extraction System for Biological Literature‖. PLoS Biol. 2(11):e309. doi:10.1371/journal.pbio.0020309. 10. Knappe R., Bulskov H., Andreasen T. (2004) Perspectives on Ontology-based Querying // International Journal of Intelligent Systems. – http://akira.ruc.dk/~knappe/publications/ ijis2004.pdf. 11. Jacso, Peter. (2010). ―The impact of Eugene Garfield through the prizm of Web of Science,‖. Annals of Library and Information Studies, Vol. 57, p. 222. 12. Christoph Meinel Serge Linckels (2007). Semantic interpretation of natural language user input to improve search in multimedia knowledge base, Information Technologies, 49(1):40–48. 13. Giorgos Stoilos, Giorgos Stamou, and Stefanos Kollias (2005) A String Metric For Ontology Alignment, Proc. of the 4rd Int. Semantic Web Conf. (ISWC), vol 3729 of LNCS, p. 624–637, Berlin. Springer. 14. Lytvyn V., DosynD., Smolarz A. (2013). An ontology based intelligent diagnostic systems of steel corrosion protection, Elektronika, Lodzj. – No. 8. – 2-13. – Pp. 22-24 (In English). 15. Lytvyn V. (2011), The similarity metric of scientific papers summaries on the basis of adaptive ontologies , Proceedings of VIIth International Conference on Perspective Technologies and Methods in MEMS Design, Polyana, Ukraine, pp. 162. (In English) 16. Lytvyn V., Pukach P., Bobyk І., Vysotska V. (2016). The method of formation of the status of personality understanding based on the content analysis, Eastern-European Journal of Enterprise Technologies, no5/2(83), 4–12. 17. Lytvyn V., Vysotska V., Veres O., Rishnyak I., and Rishnyak H. (2017). Classification Methods of Text Documents Using Ontology Based Approach, Advances in Intelligent Systems and Computing 512, Springer International Publishing AG: 229-240.uk_UA
dc.citation.conferenceComputational linguistics andintelligent systems (COLINS 2017)-
dc.coverage.placenameKharkivuk_UA
Appears in Collections:Computational linguistics and intelligent systems. – 2017 р.

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