SIMON: A Multi-strategy Classification Approach Resolving Ontology Heterogeneity – The P2P Meets the Semantic Web

Abstract

The semantic web technology is seen as a key to realizing peer-to-peer for resource discovery and service combination in the ubiquitous communication environment. However, in a Peer-to-Peer environment, we must face the situation, where individual peers maintain their own view of the domain in terms of the organization of the local information sources. Ontology heterogeneity among individual peers is becoming ever more important issues. In this paper, we propose a multi-strategy learning approach to resolve the problem. We describe the SIMON (Semantic Interoperation by Matching between ONtologies) system, which applies multiple classification methods to learn the matching between ontologies. We use the general statistic classification method to discover category features in data instances and use the first-order learning algorithm FOIL to exploit the semantic relations among data instances. On the prediction results of individual methods, the system combines their outcomes using our matching committee rule called the Best Outstanding Champion. The experiments show that SIMON system achieves high accuracy on real-world domain.

Research described in this paper is supported by The Science & Technology Committee of Shanghai Municipality Key Project Grant 02DJ14045 and by The Science & Technology Committee of Shanghai Municipality Key Technologies R&D Project Grant 03dz15027.