Ontology Mapping for Cross Domain Knowledge Transfer
The proliferation of domain specific ontologies has improved the ability to represent process and store information in regard to highly specialized domains. However, adhoc transfer of information between domain specific ontologies is not possible. Consequently, multiple solutions have been pro-posed and evaluated as means of facilitating the adhoc transfer of information between another. These range from, structural approaches, which attempt to match knowledge structures between ontologies; lexicographical approaches, that use high level reasoning to match concepts between related ontologies and finally, local structure approaches which look for similar local structures between ontologies to facilitate the transfer of information. To date, the success rate of the published algorithms has been relatively poor. Some of the most successful algorithms, at best are able to match around 50% of the concepts between related ontologies. In this paper we propose a novel global-local hybrid approach to improve the success and accuracy of adhoc information transfer between domain specific ontologies. We demonstrate the efficiency of the proposed algorithm by matching the nodes of three inter-related medical domain ontologies. This demonstrates a significant improvement over existing lexicographical and structural approaches.
