IEEE Transactions on Pattern Analysis and Machine Intelligence
SIGDOC '86 Proceedings of the 5th annual international conference on Systems documentation
Determining Semantic Similarity among Entity Classes from Different Ontologies
IEEE Transactions on Knowledge and Data Engineering
The description logic handbook: theory, implementation, and applications
The description logic handbook: theory, implementation, and applications
Word association norms, mutual information, and lexicography
ACL '89 Proceedings of the 27th annual meeting on Association for Computational Linguistics
Verbs semantics and lexical selection
ACL '94 Proceedings of the 32nd annual meeting on Association for Computational Linguistics
Ontology-Based Information Retrieval Model for the Semantic Web
EEE '05 Proceedings of the 2005 IEEE International Conference on e-Technology, e-Commerce and e-Service (EEE'05) on e-Technology, e-Commerce and e-Service
Using information content to evaluate semantic similarity in a taxonomy
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 1
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On the basis of psychological studies about similarity, we propose a model, called the fuzzy contrast model, to measure the semantic similarity between concepts expressed by OWL DL. By transforming an OWL DL concept to a set of axioms in description logic $\mathcal {S}\mathcal {H}\mathcal {O}\mathcal {I}\mathcal {N}(\mathcal {D})$, the fuzzy contrast model computes the similarity of concepts from their semantic descriptions in $\mathcal {S}\mathcal {H}\mathcal {O}\mathcal {I}\mathcal {N}(\mathcal {D})$. In order to imitate human perception of sameness and difference, fuzzy set is introduced to built intersection and set difference of feature set in our model. An iterative method is proposed to compute the similarity of concepts. Two experimental results are provided to show the effectiveness of fuzzy contrast model.