HYBRID BIOINSPIRED ALGORITHM FOR ONTOLOGIES MAPPING IN THE TASKS OF EXTRACTION AND KNOWLEDGE MANAGEMENT

Abstract

The article is devoted to solving the problem of mapping ontological models in the processes of extracting and knowledge management. The relevance and significance of this task are due to the need to maintain reliability and eliminate redundancy of knowledge during the integration (unification) of various origins structured information sources. The proximity and consistency of the conceptual semantics of the combined resource during the mapping is the main criterion for the effectiveness of the proposed solutions. The article considers the problems of choosing appropriate solution approaches that preserve semantics when displaying concepts. The strategy of choosing bio-inspired modeling is substantiated. The aspects of the effectiveness of various decentralized bio-inspired methods are analyzed. The reasons for the need for hybridization are identified. The paper proposes to solve the problem of mapping ontological models using a bio-inspired algorithm based on hybridization of bacterial and cuckoo search algorithms optimization mechanisms. The hybridization of these algorithms allowed us to combine their main advantages: a consistent bacterial search that provides a detailed study of local areas, and a significant number of the cuckoo agent during the implementation global movements of Levy flights. To evaluate the effectiveness of the proposed hybrid bio-inspired algorithm, a software product was developed and experiments were performed on the mapping of different sizes ontologies. Each concept of any ontology has a certain set of attributes, which is a semantic vector of attributes. The degree of the semantic vectors similarity for the compared concepts of displayed ontologies is a criterion for their integration. To improve the quality of the display process, a new encoding of solutions has been introduced. The quantitative estimates obtained demonstrate time savings in solving problems of relatively large dimension (from 500,000 ontograph vertices) of at least 13 %. The time complexity of the developed hybrid algorithm is O (n 2). The described studies have a high level of theoretical and practical significance and are directly related to the solution of classical problems of artificial intelligence aimed at finding hidden dependencies and patterns on a multitude of knowledge elements.