Using Information Content to Evaluate Semantic Similarity in a Taxonomy
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).
Cite
Text
Resnik. "Using Information Content to Evaluate Semantic Similarity in a Taxonomy." International Joint Conference on Artificial Intelligence, 1995.Markdown
[Resnik. "Using Information Content to Evaluate Semantic Similarity in a Taxonomy." International Joint Conference on Artificial Intelligence, 1995.](https://mlanthology.org/ijcai/1995/resnik1995ijcai-using/)BibTeX
@inproceedings{resnik1995ijcai-using,
title = {{Using Information Content to Evaluate Semantic Similarity in a Taxonomy}},
author = {Resnik, Philip},
booktitle = {International Joint Conference on Artificial Intelligence},
year = {1995},
pages = {448-453},
url = {https://mlanthology.org/ijcai/1995/resnik1995ijcai-using/}
}