The Design And Implementation Of An Ontology Matching Tool

As the emergence of Semantic Web, there are more and more ontology-based applications. The distributed nature of ontology development has led to a large number of ontologies covering overlapped domains. Ontology Matching is a process of finding similarities between ontologies. It is an important task to achieve interoperation between semantic web applications using different ontologies.As we know, both of linguistic similarity and structural similarity are important for ontology matching. In this thesis, two new approaches to ontology matching are described: one is an approach based on linguistic matching for ontologies, called LMO; the other is an approach based on graph matching for ontologies, called GMO. The main idea of LMO is constructing virtual document for each entity in the ontologies and using VSM method to gain similarities between these virtual documents. GMO utilizes bipartite graphs to represent ontologies, and measures the structural similarity between graphs by a new similarity propagation measurement. Based on the two matching approaches, we design and implement Falcon-AO, which is an automatic ontology matching tool. It uses LMO and GMO approaches to calculate similarities between entities from different ontologies. Furthermore, the performance of Falcon-AO is analyzed by experimental results. The experimental results show that Falcon-AO is in the top level among current ontology matching tools.