Using WordNet to disambiguate word senses for text retrieval
SIGIR '93 Proceedings of the 16th annual international ACM SIGIR conference on Research and development in information retrieval
Using WordNet and Lexical Operators to Improve Internet Searches
IEEE Internet Computing
Using corpus statistics and WordNet relations for sense identification
Computational Linguistics - Special issue on word sense disambiguation
An effective approach to document retrieval via utilizing WordNet and recognizing phrases
Proceedings of the 27th annual international ACM SIGIR conference on Research and development in information retrieval
An Efficient Ontology Comparison Tool for Semantic Web Applications
WI '05 Proceedings of the 2005 IEEE/WIC/ACM International Conference on Web Intelligence
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With the recent availability of large number of bioinformatics data sources, query from such databases and rigorous annotation of experimental results often use semantic frameworks in the form of an ontology. With the growing access to heterogeneous and independent data repositories, determining the semantic similarity or difference of two ontologies is critical in information retrieval, information integration and semantic web services. In this paper, a sense refinement algorithm is proposed to construct a refined sense set (RSS) for an ontology so that the senses (synonym words) in this refined sense set represent the semantic meanings of the terms used by this ontology. In addition, a semantic set that combines the refined sense set of ontology with the relationship edges connecting the terms in this ontology is proposed to represent the semantics of this ontology. With the semantic sets, measuring the semantic similarity or difference of two ontologies is simplified as comparing the commonality or difference of two sets. The experimental studies show that the proposed method of measuring the semantic similarity or difference of two ontologies is efficient and accurate.