Abstract：The mapping efficiency is the key to some dynamic mapping applications. A modularization based large-scale ontology mapping approach is proposed. Firstly, it uses a weighted semantic distance and information content based method is employed to calculate the similarity of ontology concepts. Then, by an improved efficient agglomerative hierarchical clustering algorithm, the concepts are clustered and the sub-ontologies are extracted. Finally, an elaborate information retrieval based method is designed to find related sub-ontologies from heterogeneous ontologies. The proposed approach reduces time complexity by pruning candidate search space effectively. The experimental results show that the proposed approach improves the mapping efficiency significantly with high-quality mapping results.
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