NDLI: Neighbour based structural proximity measures for ontology matching systems

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Neighbour based structural proximity measures for ontology matching systems

Content Provider	ACM Digital Library
Author	Sathiya, B. Aghila, G. Saruladha, K.
Abstract	The evolution of semantic web leads to more heterogeneity and interoperability problems among the semantic data represented by ontologies. Ontology matching techniques play an important role in semantic web. The ontology matching techniques are used to handle heterogeneity among ontologies and to establish interoperability. The effectiveness of the ontology matching systems is evaluated by the match quality measured in terms of precision and recall. The efficiency of the ontology matching systems depends on the size of the input ontologies (number of concepts in ontology). The size of the ontologies being matched influences the efficiency in terms of execution time and may lead to out of memory error. Hence improving the efficiency of ontology matching system insists on reducing the concept match space which leads to less execution time. The concept match space could be reduced by decomposing the ontologies into disjoint clusters. In this paper, two new neighbour based structural proximity measures TNSP (Tversky based Neighbour Structural Proximity) and DNSP (Dice based Neighbour Structural Proximity) are proposed to form disjoint clusters of the ontology. The proposed measures reduce the number of computations required to identify structurally similar inter ontology concepts thereby improving the efficiency. This reduction in computation is achieved as each concept is compared only with neighbour concepts. The best neighbour combination for the proposed measures TNSP and DNSP is experimentally determined. The proposed measures were evaluated experimentally on real world large ontologies (mouse and human anatomy). The experiments prove that the proposed neighbour based structural similarity measures are more efficient than the existing structural similarity measures without compromising on effectiveness.
Starting Page	1079
Ending Page	1085
Page Count	7
File Format	PDF
ISBN	9781450311960
DOI	10.1145/2345396.2345569
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2012-08-03
Publisher Place	New York
Access Restriction	Subscribed
Subject Keyword	Semantic web Ontology partitioning Ontology Ontology matching Neighbour similarity measure
Content Type	Text
Resource Type	Article

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