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Article title THE DEVELOPMENT OF GENETIC ALGORITHM FOR SEMANTIC SIMILARITY ESTIMATION IN TERMS OF KNOWLEDGE MANAGEMENT PROBLEMS
Authors Yu.A. Kravchenko, I.O. Kursitys, E.V. Kuliev
Section SECTION II. KNOWLEDGE MANAGEMENT
Month, Year 06, 2016 @en
Index UDC 002.53:004.89
DOI
Abstract This article is devoted to the development of a new approach for semantic similarity estimation to solve different knowledge management problems. Due to information flows constantly growing in various life spheres the problems of searching new ways of storing, representation, formalization, systematization and processing of information from heterogeneous sources are relevant today. The main problem in the field of knowledge search is the complexity of identification and usage of key information, which is increasing constantly. To solve this problem we propose to modify previously developed knowledge filter running on the basis of the semantic concepts taxonomy tree as an systematization of complex areas of the reality and hierarchical knowledge in order to define and arrange terms and its synonyms with a further query transformation into the most effective form. The knowledge filter meta-model is supplemented by a semantic similarity estimation block, that allows us to obtain the most appropriate results in the context of semantics. We analyzed the assigned problem, gave a definition of the term ‘semantic similarity’, and observed different ways of its estimation. To solve the problem we proposed the graph model containing components of ontology triplets. The semantic similarity formula is presented in this paper. To increase the efficiency we developed the genetic algorithm for semantic similarity estimation in accordance with the graph model. A set of genetic operators of crossover and mutation is proposed for genetic algorithm work. Experiments carried out on benchmarks show the efficiency of developed approach.

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Keywords Knowledge management; ontologies; meta-model; semantic similarity; graph model; genet-ic algorithm
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