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International Conference on Intelligent Computing

ICIC 2015: Advanced Intelligent Computing Theories and Applications pp 510–516Cite as

An Ontology-Based Approach for Measuring Semantic Similarity Between Words

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Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 9227))

Abstract

The estimation of semantic similarity between words play an important role in many language related applications. In this paper, we survey most of the ontology-based approaches in order to evaluate their advantages and limitations. We also present an approach for measuring semantic similarity. As a kind of feature-based method, proposed method extracts taxonomic features from ontology, aiming to provide a high-efficient, simple and reliable semantic similarity assessment method. We evaluate and compare our approach’s results against those reported by related works under a common framework. Result demonstrated that the proposed method has higher correlation with human subjective judgment than most of existing methods.

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Acknowledgment

This paper is supported by the National Natural Science Funds of China (61272015, 61050004), and also is supported by Henan Province basic and frontier technology research project (142300410303).

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Authors and Affiliations

  1. School of Computer Science and Technology, Wuhan University of Technology, Wuhan, Hubei, China

    Ruiling Zhang, Shengwu Xiong & Zhong Chen

  2. Luoyang Normal University, Luoyang, Henan, 471022, China

    Ruiling Zhang

Authors
  1. Ruiling Zhang
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  2. Shengwu Xiong
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  3. Zhong Chen
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Corresponding author

Correspondence to Ruiling Zhang .

Editor information

Editors and Affiliations

  1. Tongji University, Shanghai, China

    De-Shuang Huang

  2. Incheon, Korea (Republic of)

    Kyungsook Han

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© 2015 Springer International Publishing Switzerland

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Zhang, R., Xiong, S., Chen, Z. (2015). An Ontology-Based Approach for Measuring Semantic Similarity Between Words. In: Huang, DS., Han, K. (eds) Advanced Intelligent Computing Theories and Applications. ICIC 2015. Lecture Notes in Computer Science(), vol 9227. Springer, Cham. https://doi.org/10.1007/978-3-319-22053-6_54

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  • DOI: https://doi.org/10.1007/978-3-319-22053-6_54

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-22052-9

  • Online ISBN: 978-3-319-22053-6

  • eBook Packages: Computer ScienceComputer Science (R0)

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