Application of a Bioinspired Search Algorithm in Assessing Semantic Similarity of Objects from Heterogeneous Ontologies

This study focuses on addressing theoretical aspects in knowledge search management and interdisciplinary intellectual information architecture initiation, specifically in semantically directed search. The objective is to develop promising approaches in computer science and information retrieval systems, integrating knowledge from chaotic clusters into subject domains for modeling new information systems. The study's significance lies in its proposed solution to the examined problem, applicable to various NP-hard problems and expanding the use of information-intelligent ordered clusters. The article explores enhancing the efficiency of search algorithms for semantic similarity analysis in expert linguistic information, initializing from subject-specific text collections for use in intellectual information systems. Current methods struggle with semantic integration due to complexity. Heuristic methods using resultant ontologies commonly address semantic heterogeneity. This study suggests bioinspired search algorithms to tackle semantic data heterogeneity. The modified white rabbit algorithm is employed, enabling semantic-level data analysis, subject domain identification, and interaction understanding. It facilitates interaction between information systems based on a unified ontology. Semantic search is pivotal for knowledge management technology development. Diverse search architectures enhance search quality and efficiency. Modified algorithms based on this research can improve result quality. Software modules were developed to simulate the proposed algorithm, and results were analyzed and compared with classical algorithms, affirming the proposed solution's effectiveness.