Using Information Content to Evaluate Semantic Similarity in a Taxonomy (1995) [374 citations — 5 self]
by
Philip Resnik
In Proceedings of the 14th International Joint Conference on Artificial Intelligence
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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},
year = {1995},
pages = {448--453}
}
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},
year = {1995},
pages = {448--453}
}
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Abstract:
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).
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