1998 AnInformationTheoDefOfSim

(Lin, 1998) ⇒ Dekang Lin. (1998). “An Information-Theoretic Definition of Similarity.” In: Proceedings of the 15th International Conference on Machine Learning (ICML 1998).

Subject Headings: Lin Similarity Measure, Lexical Semantic Similarity Function.

Notes

Cited By

~1585 http://scholar.google.com/scholar?q=%22An+Information-Theoretic+Definition+of+Similarity%22+1998

2002

(Doan et al., 2002) ⇒ AnHai Doan, Jayant Madhavan, Pedro Domingos, and Alon Y. Halevy. (2002). “Learning to Map Between Ontologies on the Semantic Web.” In: Proceedings of the 11th International Conference on World Wide Web (WWW 2002). doi:10.1145/511446.511532
- QUOTE: The similarity measure in (Ichise et al., 2001) is based on κ statistics, and can be through of as being defined over the joint probability distribution of the concepts involved. In (Lin, 1998b) the authors propose an information-theoretic notion of similarity that is based on the joint distribution. These works argue for a single best universal similarity measure, whereas GLUE allows for application-dependent similarity measures.

Quotes

Abstract

Similarity is an important and widely used concept. Previous definitions of similarity are tied to a particular application or a form of knowledge representation. We present an informationtheoretic definition of similarity that is applicable as long as there is a probabilistic model. We demonstrate how our definition can be used to measure the similarity in a number of different domains.,

	Author	volume	Date Value	title	type	journal	titleUrl	doi	note	year
1998 AnInformationTheoDefOfSim	Dekang Lin			An Information-Theoretic Definition of Similarity			http://web.cs.ualberta.ca/~tszhu/webmining/paper/ir/sim.pdf