Article

Article title THE EVOLUTIONARY APPROACH FOR ONTOLOGIES INTEGRATION PROBLEM
Authors V.V. Bova, D.V. Zaruba, V.V.Kureichik
Section SECTION I. EVOLUTIONARY MODELLING, GENETIC AND BIONIC ALGORITHMS
Month, Year 06, 2015 @en
Index UDC 004.822
DOI
Abstract Currently, the integration of data and knowledge is one of the most important problems of information systems interoperability maintenance at the structural and semantic level. The article discusses the technology of ontologies integration, involves a comparison of automatic ontology concepts via the composite semantic metric (names and meanings of concepts or its contexts), sets of attributes and their position in the structure of the original ontology. The authors proposed an evolutionary approach to the problem of integrating multiple ontologies for interoperability and representation of data and knowledge in intelligent information systems. This approach allows us to define semantically priority data and knowledge objects to represent them in the model of integration as well as to eliminate duplication and contradictions of entities and relationships at the level of the domain and data objects from the integration. Knowledge integration problems belong to the class of NP-hard optimization problems, and can be implemented using genetic algorithms to find optimal solutions. From a mathematical point of view the process of parametric optimization comes down to the task of evaluating the semantic proximity of knowledge objects of heterogeneous ontologies based on the harmonization of the attribute, the taxonomic and relational similarity measure. The proposed genetic algorithm is based on the use of analogues to the evolutionary processes of reproduction, crossover, mutation and natural selection. For the analysis of the developed approach, a series of experiments. The findings confirmed the theoretical significance and application prospects of this approach, as well as possible to establish the optimal parameters of the algorithm. For the analysis of the developed approach, a series of experiments. The findings confirmed the theoretical significance and application prospects of this approach, as well as possible to establish the optimal parameters of the algorithm.

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Keywords Data and knowledge integration; semantic proximity; ontology; genetic algorithm; genetic operators; relational; taxonomic and attribute similarity measure.
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