Determining Semantic Similarity among Entity Classes from Different Ontologies

By Michael A. Rodriguez, Max J. Egenhofer. IEEE Transactions on Knowledge and Data Engineering, 2003

1,016 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/500923d2513d30299350a6a0e9b84b077250dc78

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In Artificial Intelligence

Citations in the corpus

Semantic E-Workflow Composition (2003, background): “autonomous systems are required to commit to a shared ontology, and compromises are difficult to maintain when new concepts are added (Rodrı́guez and Egenhofer, 2002).”
Evolution of Semantic Similarity—A Survey (2020, background): “Some of the terms may not be included in one ontology, which provides a scope to use multiple ontologies to calculate their relationship [81].”
Ontology Mapping - An Integrated Approach (2004, neutral): “[20] give a general overview of similarity.”
Semantic similarity methods in wordNet and their application to information retrieval on the web (2005, background): “Hybrid methods combine the above ideas [19].”
GMO: A Graph Matching for Ontologies (2005, background): “In the literature [14], semantic-neighborhood matching is combined with word matching for class comparison.”
Sentence similarity based on semantic nets and corpus statistics (2006, methodology): “Generally, these methods can be categorized into two groups: edge counting-based (or dictionary/thesaurus-based) methods and information theory-based (or corpus-based) methods; a detailed review on word similarity can be found in [20], [34].”
Ontology-based semantic similarity: A new feature-based approach (2012, background): “In this respect, several of the measures proposed in related works (especially weighted and featurebased ones (Li, et al., 2003; Rodríguez & Egenhofer, 2003; Tversky, 1977)), do not accomplish basic properties such as minimality, hampering their applicability in some data mining algorithms.”