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.