An Effective Similarity Propagation Method for Matching Ontologies without Sufficient or Regular Linguistic Information

Authors:
Peng Wang;Baowen Xu
Affiliations:
College of Software Engineering, Southeast University, China and State Key Laboratory for Novel Software Technology, Nanjing University, China;State Key Laboratory for Novel Software Technology, Nanjing University, China and Department of Computer Science and Technology, Nanjing University, China
Venue:
ASWC '09 Proceedings of the 4th Asian Conference on The Semantic Web
Year:
2009

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Abstract

Most existing ontology matching methods are based on the linguistic information. However, some ontologies have not sufficient or regular linguistic information such as natural words and comments, so the linguistic-based methods can not work. Structure-based methods are more practical for this situation. Similarity propagation is a feasible idea to realize the structure-based matching. But traditional propagation does not take into consideration the ontology features and will be faced with effectiveness and performance problems. This paper analyzes the classical similarity propagation algorithm Similarity Flood and proposes a new structure-based ontology matching method. This method has two features: (1) It has more strict but reasonable propagation conditions which make matching process become more efficient and alignments become better. (2) A series of propagation strategies are used to improve the matching quality. Our method has been implemented in ontology matching system Lily. Experimental results demonstrate that this method performs well on the OAEI benchmark dataset.