Computing semantic similarity based on novel models of semantic representation using Wikipedia

•Two semantic models for the representation of concepts are proposed.•The presented semantic models focus on Wikipedia Category Graph and hyperlinks.•Several methods to semantic similarity computation for concepts are provided. Computing Semantic Similarity (SS) between concepts is one of the most c...

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Bibliographic Details
Published in:Information processing & management 2018-11, Vol.54 (6), p.1002-1021
Main Authors: Qu, Rong, Fang, Yongyi, Bai, Wen, Jiang, Yuncheng
Format: Article
Language:English
Subjects:
Artificial intelligence
Benchmarks
Computation
Concept similarity
Domains
Feature-based methods
Information content
Judgments
Measurement methods
Natural language processing
Neural networks
Representations
Semantic similarity
Semantics
Similarity
Wikipedia
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Summary:•Two semantic models for the representation of concepts are proposed.•The presented semantic models focus on Wikipedia Category Graph and hyperlinks.•Several methods to semantic similarity computation for concepts are provided. Computing Semantic Similarity (SS) between concepts is one of the most critical issues in many domains such as Natural Language Processing and Artificial Intelligence. Over the years, several SS measurement methods have been proposed by exploiting different knowledge resources. Wikipedia provides a large domain-independent encyclopedic repository and a semantic network for computing SS between concepts. Traditional feature-based measures rely on linear combinations of different properties with two main limitations, the insufficient information and the loss of semantic information. In this paper, we propose several hybrid SS measurement approaches by using the Information Content (IC) and features of concepts, which avoid the limitations introduced above. Considering integrating discrete properties into one component, we present two models of semantic representation, called CORM and CARM. Then, we compute SS based on these models and take the IC of categories as a supplement of SS measurement. The evaluation, based on several widely used benchmarks and a benchmark developed by ourselves, sustains the intuitions with respect to human judgments. In summary, our approaches are more efficient in determining SS between concepts and have a better human correlation than previous methods such as Word2Vec and NASARI.
ISSN:0306-4573
1873-5371
DOI:10.1016/j.ipm.2018.07.002