A novel family of IC-based similarity measures with a detailed experimental survey on WordNet. (November 2015)

• Record Type: Journal Article
• Authors: Lastra-Díaz, Juan J.; García-Serrano, Ana
• Is Part Of: Engineering applications of artificial intelligence. Volume 46:Part A(2015:Oct.)
• Page Start: 140
• Page End: 153
• Publication Date: 2015-11
• Subjects: Ontology-based semantic similarity measures -- IC-based measures -- Semantic similarity -- Intrinsic and corpus-based information content models -- Jiang–Conrath distance -- Semantic similarity on WordNet survey; Engineering -- Data processing -- Periodicals; Artificial intelligence -- Periodicals; Expert systems (Computer science) -- Periodicals; Ingénierie -- Informatique -- Périodiques; Intelligence artificielle -- Périodiques; Systèmes experts (Informatique) -- Périodiques; Artificial intelligence; Engineering -- Data processing; Expert systems (Computer science); Periodicals; 620.00285
• DOI: 10.1016/j.engappai.2015.09.006
• Languages: English
• ISSNs: 0952-1976

Abstract

Abstract: This paper introduces a novel family of ontology-based similarity measures based on the Information Content (IC) theory, a detailed state of the art, a large experimental survey into ontology-based similarity measures on WordNet, and a new comparison between intrinsic and corpus-based IC models. Our experiments are based on our implementation of a large set of similarity measures, intrinsic and corpus-based IC models, which are evaluated on two known datasets and three different WordNet versions. The new measures are called weighted Jiang–Conrath distance ( wJ & Cdist ) and similarity ( wJ & Csim ), cosine-normalized Jiang–Conrath similarity ( cosJ & Csim ) and cosine-normalized weighted Jiang–Conrath similarity ( coswJ & Csim ). Two of our similarity measures outperform the state-of-the-art measures on the RG65 dataset, and one of them obtains the third overall score on all the datasets and evaluated WordNet versions. The cosine-normalized similarity measures are a non-linear normalization of the classic Jiang–Conrath (J&C) distance and the new wJ & C distance. On the other hand, the wJ & C distance is a generalization of the classic J&C distance which is based on the length of the shortest path between concepts within an IC-based weighted graph. Our measures are based on two not previously considered notions: (1) a generalization of the classic J&C distance to any type of taxonomy, based on an IC-based weighted graph derived from the conditional probabilities between child and parent concepts, and (2) a non-linear normalization function that converts the ontology-based semantic distances into similarity functions. Finally, the corpus-based IC models based on the Resnik method obtain rivaling results as regards the state-of-the-art intrinsic IC models, when they are used with some unexplored WordNet-based frequency files. Therefore, this latter fact allows us to reconsider some previous conclusions about the outperformance of the intrinsic IC models over the corpus-based ones.