Title: A clustering-based ontology matching technique using cognitive theory and concept importance

Authors: K. Saruladha; S. Ranjini

Addresses: Department of Computer Science and Engineering, Pondicherry Engineering College, Puducherry 605014, India ' Department of Computer Science and Engineering, Pondicherry Engineering College, Puducherry 605014, India

Abstract: Ontology matching techniques play a crucial role in solving semantic heterogeneity problem. These techniques have gained more importance because of the development of numerous ontologies developed for the same domain by different domain experts. The perception of concepts, relationships and context varies from one expert to other. This paper proposes a method of ontology matching which is based on the clustering method. The proposed ontology matching algorithm partitions the large ontologies into clusters using traditional hierarchical agglomerative clustering (HAC) and concept algebra-based clustering algorithm. The work reported in this paper is threefold: i) reduction of ontology concept match by formation of ontology clusters using HAC and formation of ontology clusters using three dimensions of similarity viz. structural similarity, attribute similarity and semantic comprehensive correlation degree which is perception conceived from cognitive algebra theory; ii) identification of quality clusters using the concept importance measure which quantifies importance of concepts using concept types and relationship types; iii) a refactoring technique is used to analyse how the precision of the ontology matching system improves by implementing renaming operation (RN). The proposed ontology matching system is evaluated using OAEI benchmark datasets.

Keywords: heterogeneous; concept algebra; hierarchical clustering; concept importance; refactoring; similarity measures; ontology.

DOI: 10.1504/IJKESDP.2016.084605

International Journal of Knowledge Engineering and Soft Data Paradigms, 2016 Vol.5 No.3/4, pp.316 - 340

Received: 06 Jun 2016
Accepted: 04 Dec 2016
Published online: 17 Jun 2017 *

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