@inproceedings{Lee2008,
abstract = {Semantic-similarity measures quantify concept similarities in a given ontology. Potential applications for these measures include search, data mining, and knowledge discovery in database or decision-support systems that utilize ontologies. To date, there have not been comparisons of the different semantic-similarity approaches on a single ontology. Such a comparison can offer insight on the validity of different approaches. We compared 3 approaches to semantic similarity-metrics (which rely on expert opinion, ontologies only, and information content) with 4 metrics applied to SNOMED-CT. We found that there was poor agreement among those metrics based on information content with the ontology only metric. The metric based only on the ontology structure correlated most with expert opinion. Our results suggest that metrics based on the ontology only may be preferable to information-content-based metrics, and point to the need for more research on validating the different approaches.},
author = {Lee, Wei-Nchih and Shah, Nigam and Sundlass, Karanjot and Musen, Mark},
booktitle = {AMIA - Annual Symposium proceedings / AMIA Symposium. AMIA Symposium},
issn = {1942-597X},
keywords = {Algorithms,Artificial Intelligence,Automated,Automated: methods,California,Natural Language Processing,Pattern Recognition,SML-LIB-BIBLIO,Semantics,Systematized Nomenclature of Medicine,lang:ENG},
mendeley-tags = {SML-LIB-BIBLIO,lang:ENG},
month = jan,
pages = {384--8},
pmid = {18999312},
title = {{Comparison of ontology-based semantic-similarity measures.}},
url = {http://www.pubmedcentral.nih.gov/articlerender.fcgi?artid=2655943\&tool=pmcentrez\&rendertype=abstract},
year = {2008}
}