Using information content to evaluate semantic similarity in a taxonomy

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Using information content to evaluate semantic similarity in a taxonomy (1995)

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by Philip Resnik

Venue:	In Proceedings of the 14th International Joint Conference on Artificial Intelligence (IJCAI-95
Citations:	1097 - 8 self

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@INPROCEEDINGS{Resnik95usinginformation,
    author = {Philip Resnik},
    title = {Using information content to evaluate semantic similarity in a taxonomy},
    booktitle = {In Proceedings of the 14th International Joint Conference on Artificial Intelligence (IJCAI-95},
    year = {1995},
    pages = {448--453},
    publisher = {Morgan Kaufmann}
}

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Abstract

philip.resnikfleast.sun.com This paper presents a new measure of semantic similarity in an IS-A taxonomy, based on the notion of information content. Experimental evaluation suggests that the measure performs encouragingly well (a correlation of r = 0.79 with a benchmark set of human similarity judgments, with an upper bound of r = 0.90 for human subjects performing the same task), and significantly better than the traditional edge counting approach (r = 0.66). 1

Keyphrases

information content semantic similarity benchmark set new measure experimental evaluation is-a taxonomy human similarity judgment upper bound traditional edge counting approach human subject

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