DOI:https://10.15407/kvt205.03.005
Cybernetics and Computer Engineering, 2021, 3(205)
GRITSENKO V.I.1, Corresponding Member of NAS of Ukraine,
Director of International Research and Training Center for Information Technologies and Systems of the National Academy of Sciences of Ukraine and Ministry of Education and Science of Ukraine
ORCID: 0000-0003-4813-6153
e-mail: vig@irtc.org.ua
GLADUN A.Ya.1, PhD (Engineering),
Senior Researcher of the Department of Complex Research of Information Technologies and Systems
ORCID: 0000-0002-4133-8169
e-mail: glanat@yahoo.com
KHALA K.O.1,
Researcher of the Department of Complex Research of Information Technologies and Systems
ORCID: 0000-0002-9477-970X
e-mail: cecerongreat@ukr.net
Martínez-Béjar R.2, PhD (Informatics),
Professof at the Department of Information and Communication Engineering
ORCID: 0000-0002-9677-7396
e-mail: rodrigo@um.es
1 International Research and Training Center for Information Technologies and Systems of the National Academy of Sciences of Ukraine and Ministry of Education and Science of Ukraine, 40, Acad. Glushkov av., Kyiv, 03187, Ukraine
2 Department of Information and Communication Engineering and Artificial Intelligence University of Murcia CP 30180 Bullas, Spain
SEMANTICAL SIMILARITY EVALUATION METHOD OF CONCEPTS FOR COMPARISON OF ONTOLOGIES IN APPLIED PROBLEMS OF ARTIFICIAL INTELLIGENCE
Introduction. The expediency of reapplication of ontology in applied intelligent information systems (IIS), which are focused on functioning in the open Web environment on the basis of Semantic Web technologies, is substantiated in the work. Features of ontology storage and management platforms and their metadata are analyzed. Possibilities of searching in ontology repositories and their reuse in IIS are considered. The mechanisms of ontology search based on semantic processing of their metadata, analysis of ontology structure using metrics of semantic similarity between their concepts related to the current user task are presented.
The purpose of the article is the development of algorithms and methods for evaluating semantic models, which consist in combining qualitative (ontological) representation of knowledge with quantitative (numerical) evaluation of ontologies and their parameters (semantic proximity, semantic distance, semantic affinity) aimed at finding similarities different ontologies
Methods. Methods of ontological analysis of objects of the subject area, theoretical and multiple approaches to determine the degree of closeness of two objects by comparing their properties (feature matching) and traditional methods of statistical analysis are used to solve the tasks set in the work.
Results. The proposed method of estimating semantic similarity allows on the basis of semantic analysis of natural annotations of metadata both ontologies and data (including Big Data) to perform the task of their interpretation and selection to the problem to be solved by the applied IIS or application. The obtained results allow to create original IIS for artificial intelligence in economics, medicine, national security, defense and social sphere.
Conclusion. We proposed an original approach to the evaluation and analysis of metadata (ontologies, data), based on semantic analysis of metadata and determining the semantic similarity of structural data models (ontologies, data) and the formation of a ranked set of related ontologies to solve problems of artificial intelligence. The application of methods for defining semantically similar concepts is presented as a tool for semantic comparison of the structure of ontologies, which were found in the repository under formal conditions, with a poorly structured PM-description. At present, there is no generally accepted standard for presenting metadata, so the proposed methods of analysis of PM annotations are the most adequate means of comparing the semantics of ontologies, data with the problems for which they can be used.
Keywords: semantic similarity, formal ontology model, metadata, metadata standards, intelligent information system, ontology repository.
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Received 11.01.2021