Research On Heterogeneous Optology Merging Method Based On Synthetic Concept Similarity

Ontology has become a hot research topic in the fields of artificial intelligence, knowledge representation. semantic Web, data integration and semantic interoperability in recent years. With the widespread application of ontology, how to solve the integration of the heterogeneous ontology has become a thorny question, the effective way to solve heterogeneous integration in the ontology is ontology mapping or ontology merging.An approach for merging heterogeneous ontology based on WordNet and synthetic concept similarity is proposed in this paper. According to the characteristic of the ontology and concept, an improved method of concept similarity computation is designed and implemented, separately computes the similarity of concept instance, concept name, concept attribute and concept relation, and reasonable uses of the composite strategy of combined results to obtain the synthetic similarity between concepts. The method of the structure and the basic principles of WordNet, as well as the approach of heterogeneous ontology merging by use WordNet based on synthetic concept similarity, is discussed in this paper.Finally, the approach of ontology merging is verified by a typical test case. We building the ontology model by use the popular ontology construction tools Protege2000 and OWL ontology language, synthetically computing the similarity of concept instance, concept name, concept attribute and concept relation. Using the values obtained throughout the previous steps, the two or more heterogeneous ontologies are merged based on the WordNet.