by Philip Resnik
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).
Reference:
Using Information Content to Evaluate Semantic Similarity in a Taxonomy (Philip Resnik), In Proceedings of the 14th International Joint Conference on Artificial Intelligence IJCAI, volume 1, 1995.
Bibtex Entry:
@inproceedings{Resnik1995,
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).},
archivePrefix = {arXiv},
arxivId = {cmp-lg/9511007},
author = {Resnik, Philip},
booktitle = {Proceedings of the 14th International Joint Conference on Artificial Intelligence IJCAI},
eprint = {9511007},
keywords = {Information Content,SML-LIB-BIBLIO,Semantic Similarity,information content,lang:ENG,semantic similarity},
mendeley-tags = {SML-LIB-BIBLIO,information content,lang:ENG,semantic similarity},
pages = {448--453},
primaryClass = {cmp-lg},
title = {{Using Information Content to Evaluate Semantic Similarity in a Taxonomy}},
volume = {1},
year = {1995}
}