Enhancing performance and accuracy of ontology integration by propagating priorly matchable concepts

Previous work on ontology integration involves only blind or exhaustive matching among all the concepts in different ontologies. Therefore, the computational complexity rapidly increases in integrating large ontologies. In addition, semantic mismatches, logical inconsistencies, and conceptual conflicts in ontology integration have not yet become avoidable. The aim of this paper is to investigate a method to reduce the computational complexity and enhance accurate matching ontology. In this paper, a novel approach has been proposed using propagating Priorly Matchable Concepts (PMCs). The key idea of our approach is analyzing multiple contexts, including the role of “natural categories”, relations, and constraints among concepts to provide additional suggestions for possible matching concepts. PMC is a collection of pairs of concepts across two different ontologies in the same Concept Types 1 that are arranged in descending order of Concept Importance 2 distances for the pairs. PMC guides on how to priorly check the similarity between concepts. It is useful to avoid checking similarities among unmatchable concepts. In addition, dependency rules are applied to filter mismatches in PMC during the integration process. Our experiments compare the computational complexity and accurate matching to previous approaches. The use of PMC as a pre-process in the integration process enhances both complexity and accuracy compared to unused PMC.