Evaluating semantic similarity and relatedness between concepts by combining taxonomic and non-taxonomic semantic features of WordNet and Wikipedia

Many applications in cognitive science and artificial intelligence utilize semantic similarity and relatedness to solve difficult tasks such as information retrieval, word sense disam-biguation, and text classification. Previously, several approaches for evaluating concept similarity and relatedness based on WordNet or Wikipedia have been proposed. WordNet-based methods rely on highly precise knowledge but have limited lexical cover-age. In contrast, Wikipedia-based models achieve more coverage but sacrifice knowledge quality. Therefore, in this paper, we focus on developing a comprehensive semantic simi-larity and relatedness method based on WordNet and Wikipedia. To improve the accuracy of existing measures, we combine various taxonomic and non-taxonomic features of WordNet, including gloss, lemmas, examples, sister-terms, derivations, holonyms/mero-nyms, and hypernyms/hyponyms, with Wikipedia gloss and hyperlinks, to describe con-cepts. We present a novel technique for extracting 'is-a' and 'part-whole' relationships between concepts using the Wikipedia link structure. The suggested technique identifies taxonomic and non-taxonomic relationships between concepts and offers dense vector representations of concepts. To fully exploit WordNet and Wikipedia's semantic attributes, the proposed method integrates their semantic knowledge at feature-level, combining semantic similarity and relatedness into a single comprehensive measure. The experimen-tal results demonstrate the effectiveness of the proposed method over state-of-the-art measures on various gold standard benchmarks. (c) 2023 Elsevier Inc. All rights reserved.

Automated summary

This paper proposes a comprehensive method for semantic similarity and relatedness based on WordNet and Wikipedia. By integrating the semantic knowledge of both resources at the feature level, the proposed method combines semantic similarity and relatedness into a single measure. Experimental results demonstrate its effectiveness over existing measures on various benchmarks.