The vocabulary problem in human-system communication
Communications of the ACM
An Information-Theoretic Definition of Similarity
ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
Ontologies Improve Text Document Clustering
ICDM '03 Proceedings of the Third IEEE International Conference on Data Mining
Verbs semantics and lexical selection
ACL '94 Proceedings of the 32nd annual meeting on Association for Computational Linguistics
Measuring semantic similarity in the taxonomy of WordNet
ACSC '05 Proceedings of the Twenty-eighth Australasian conference on Computer Science - Volume 38
WordNet: similarity - measuring the relatedness of concepts
AAAI'04 Proceedings of the 19th national conference on Artifical intelligence
Using information content to evaluate semantic similarity in a taxonomy
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 1
Semantic-based grouping of search engine results using WordNet
APWeb/WAIM'07 Proceedings of the joint 9th Asia-Pacific web and 8th international conference on web-age information management conference on Advances in data and web management
Correlation based multi-document summarization for scientific articles and news group
Proceedings of the International Conference on Advances in Computing, Communications and Informatics
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Semantic similarity is becoming a generic issue in a variety of applications in area of information retrieval (IR). Most of the researchers are using ontology as a tool for finding semantic similarities. Use of ontology allows terms in documents to be replaced by the concepts. The concepts are generally selected by identifying semantically related terms and finding a suitable term (concept) to replace them. Several approaches have been proposed for finding concepts by selecting semantically related terms, however no attempt has been made to automatise the process. The motivation of this paper is to suggest an automatic method of identifying the concepts from documents using hypernym relationship in ontologies and propose an algorithm for the same. WordNet ontology has been used for implementing the algorithm. The algorithm can be used for finding document concepts and clustering the documents based on these concepts.