Publication | Closed Access
Determining semantic similarity among entity classes from different ontologies
888
Citations
47
References
2003
Year
Ontology MatchingEngineeringSimilarity MeasureSemanticsSemantic WebCorpus LinguisticsText MiningNatural Language ProcessingInformation RetrievalData ScienceOntology MergingData IntegrationOntology LearningLanguage StudiesSingle OntologyOntology AlignmentSemantic Similarity MeasuresLinguisticsSemantic Similarity
Semantic similarity measures are crucial for information retrieval and integration, yet traditional methods compute distances within a single ontology that is either domain‑independent or a merged ontology. The study proposes a semantic similarity approach that removes the single‑ontology requirement and accommodates varying explicitness and formalization across ontologies. The approach employs a similarity function that matches synonym sets, semantic neighborhoods, and distinguishing features categorized as parts, functions, and attributes. Experimental results demonstrate that the model yields good performance with complete, detailed ontologies, and that word and neighborhood matching detect equivalent classes while feature matching discriminates similar but non‑equivalent classes.
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.
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