Determining Semantic Similarity Among Entity Classes from Different
Ontologies
Andrea Rodríguez and
Max Egenhofer IEEE Transactions on Knowledge and Data Engineering 15 (2): 442-456, 2003.
Abstract
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.
Full article
Andrea Rodríguez and
Max Egenhofer
