The increasing interest in the Semantic Web is producing a growing number of publicly available domain ontologies. These ontologies are a rich source of information that could be very helpful during the process of engineering other domain ontologies. We present an automatic technique that, given a set of Web documents, selects appropriate domain ontologies from a collection of pre-existing ontologies. We empirically compare an ontology match score that is based on statistical techniques with simple keyword matching algorithms. The algorithms were tested on a set of 183 publicly available ontologies and documents representing ten different domains. Our algorithm was able to select the correct domain ontology as the top ranked ontology 8 out of 10 times.
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