GMO: A Graph Matching for Ontologies.

Published 1 January 2005

Wei Hu, Ningsheng Jian, Yuzhong Qu, Yanbing Wang

Citations116

TL;DR

GMO uses bipartite graphs to represent ontologies, and measures the structural similarity between graphs by a new measurement, and can take a set of matched pairs, which are typically previously found by other approaches, as external input in matching process.

Abstract

Ontology matching is an important task to achieve inter-operation between semantic web applications using dif-ferent ontologies. Structural similarity plays a central role in ontology matching. However, the existing ap-proaches rely heavily on lexical similarity, and they mix up lexical similarity with structural similarity. In this paper, we present a graph matching approach for on-tologies, called GMO. It uses bipartite graphs to repre-sent ontologies, and measures the structural similarity between graphs by a new measurement. Furthermore, GMO can take a set of matched pairs, which are typi-cally previously found by other approaches, as external input in matching process. Our implementation and experimental results are given to demonstrate the effec-tiveness of the graph matching approach.

Keywords

Computer ScienceDecision Sciences

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