An Information-Theoretic Definition of Similarity
ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
An Approach for Measuring Semantic Similarity between Words Using Multiple Information Sources
IEEE Transactions on Knowledge and Data Engineering
Verbs semantics and lexical selection
ACL '94 Proceedings of the 32nd annual meeting on Association for Computational Linguistics
Semantic similarity methods in wordNet and their application to information retrieval on the web
Proceedings of the 7th annual ACM international workshop on Web information and data management
Using information content to evaluate semantic similarity in a taxonomy
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 1
Extraction of contextual information from medical case research report using WordNet
COMPUTE '11 Proceedings of the Fourth Annual ACM Bangalore Conference
Research on automatic acquisition method of Chinese domain ontology backbone based on Hownet
International Journal of Wireless and Mobile Computing
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Semantic similarity is becoming a generic issue in variety of applications in areas of information retrieval, computational linguistic and AI, both in the academia and industry. Examples include: computing semantic similarity, word sense disambiguation, text segmentation, multimodal document retrieval, image retrieval, etc. However, semantic similarity measures have been used showing mixed chances of success. The basic problem is that if semantic measures are used bluntly without understanding, they might decrease retrieval efficiency. There is a need to investigate semantic similarity approaches in order to have better understanding of these approaches. Several semantic methods for determining semantic similarity between terms have been proposed in the literature and most of them have been tested on WordNet. In this paper, we investigate the approaches to compute semantic similarity by mapping word concepts to WordNet ontology and by examining their relationship in that ontology. The paper then provides specific examples for explaining these approaches Further, the paper categorises and compares various approaches for measuring semantic similarity using WordNet ontology.