A Semantic Similarity Evaluation Method and a Tool Utilised in Security Applications Based on Ontology Structure and Lexicon Analysis

Abstract

This paper discusses a semantic similarity evaluation method within semantic models represented as ontologies or an instance bases. The capabilities of the method can be used for semantic pattern recognition within knowledge bases, which can be utilised by analytical tools especially in the security domain. The specificity of security applications requires methods for analysis of hidden, in direct, comprehensive and versatile data in search for new knowledge. Proposed method and its implementation in form of ETOSE plugin serves as an analytical process evaluating instance bases. The mechanisms has been designed to operate as a data flow interceptor, collecting the data and transforming them into instances expressed in a specific domain ontology (set of ontology modules). After such migration ETOSE plugin performs evaluation of instance base and provides the analyst mechanisms for identification of hidden associations and patterns. The quantitative approach has been applied in financial fraud identification tasks where certain templates of behaviour and associations can be described. Proposed method and tool utilize structural and lexicon comparison of compared ontologies in order to deliver multicriteria evaluation of concepts, relationships and indirectly implemented axioms. The paper demonstrates the theoretical side of designed method as well as practical examples, developed ontologies and analytical environment application.