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A unified framework for semantic similarity computation of concepts

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Abstract

Semantic similarity assessment between concepts is an important task in many language related applications. In the past, many approaches to assess similarity of concepts have been proposed by using one knowledge source. In this paper, some limitations of the existing similarity measures are identified. To tackle these problems, we propose an extensive study for semantic similarity of concepts from which a unified framework for semantic similarity computation is presented. Based on our framework, we give some generic and flexible approaches to semantic similarity measures resulting from instantiations of the framework. In particular, we obtain some new approaches to similarity measures that existing methods cannot deal with by introducing multiple knowledge sources. The evaluation based on eight benchmarks, three widely used benchmarks (i.e., M&C, R&G, and WordSim-353 benchmarks) and five benchmarks developed in ourselves (i.e, Jiang-1, Jiang-2, Jiang-3, Jiang-4, and Jiang-5 benchmarks), sustains the intuitions with respect to human judgements. Overall, some methods proposed in this paper have a good human correlation (Pearson correlation with human judgments and Spearman correlation with human judgments) and constitute some effective ways of determining semantic similarity between concepts.

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Notes

  1. https://en.wikipedia.org/

  2. http://wordnet.princeton.edu/

  3. https://www.nlm.nih.gov/mesh/

  4. http://www.disease-ontology.org/

  5. http://human-phenotype-ontology.github.io/

  6. http://www.geneontology.org/

  7. http://obi-ontology.org/

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Acknowledgements

The authors would like to thank the anonymous referees for their valuable comments and suggestions which greatly improved the exposition of the paper. The works described in this paper are supported by The National Natural Science Foundation of China under Grant Nos. 61772210 and U1911201; Guangdong Province Universities Pearl River Scholar Funded Scheme (2018); The Project of Science and Technology in Guangzhou in China under Grant Nos. 201807010043 and 202007040006. Thanks for my students Rong Qu, Yongyi Fang, and Yudong Liu for their discussion, programming, and experiments.

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    Yuncheng Jiang

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Jiang, Y. A unified framework for semantic similarity computation of concepts. Multimed Tools Appl 80, 32335–32378 (2021). https://doi.org/10.1007/s11042-021-10966-1

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