Improving ontology matchers utilizing linguistic ontologies: an information retrieval approach

Matching ontologies is a crucial process when facilitating system interoperability and information exchange. A reoccurring problem in this process is that names can be ambiguous, yielding uncertainty to whether entities of two heterogeneous ontologies are actually related. Linguistic ontologies provide a clear structure of meanings, rather than names, allowing the quantification of the relatedness of any two given meanings. We propose an approach for the automatic allocation of correct meanings within a linguistic ontology through the use of virtual documents and information retrieval techniques. The benefits of this approach are tested and established using a data set from the Ontology Alignment Evaluation Initiative (OAEI) competition, while further improvements are revealed using a benchmark data set from the same competition.