Issue No. 02 - March/April (2003 vol. 15)
M. Andrea Rodríguez , IEEE
Max J. Egenhofer , IEEE Computer Society
<p><b>Abstract</b>—Semantic similarity measures play an important role in information retrieval and information integration. Traditional approaches to modeling semantic similarity compute the semantic distance between definitions within a single ontology. This single ontology is either a domain-independent ontology or the result of the integration of existing ontologies. We present an approach to computing semantic similarity that relaxes the requirement of a single ontology and accounts for differences in the levels of explicitness and formalization of the different ontology specifications. A similarity function determines similar entity classes by using a matching process over synonym sets, semantic neighborhoods, and distinguishing features that are classified into parts, functions, and attributes. Experimental results with different ontologies indicate that the model gives good results when ontologies have complete and detailed representations of entity classes. While the combination of word matching and semantic neighborhood matching is adequate for detecting equivalent entity classes, feature matching allows us to discriminate among similar, but not necessarily equivalent entity classes.</p>
Semantic similarity, ontology integration, information integration, semantic interoperability, semantic matching.
M. A. Rodríguez and M. J. Egenhofer, "Determining Semantic Similarity among Entity Classes from Different Ontologies," in IEEE Transactions on Knowledge & Data Engineering, vol. 15, no. , pp. 442-456, 2003.