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Semantic measure of plagiarism using a hierarchical graph model

Author

Listed:
  • Tingting Zhang

    (Nanjing University)

  • Baozhen Lee

    (Nanjing Audit University)

  • Qinghua Zhu

    (Nanjing University)

Abstract

Traditional plagiarism detection is based primarily on methods of character matching or topic similarity. Another promising methodology remains largely unexplored: employing deep mining to establish a contextual hierarchy among themes. This paper proposes a semantic approach to measuring the extent of plagiarism, based on a hierarchical graph model. The main innovations are as follows: (1) hierarchical extraction of topic feature terms and elucidation of a corresponding graph structure; (2) graph similarity calculation based on the maximum common subgraph. This semantic-measure method goes beyond semantic detection of topics to take into account the context of topic feature terms, as well as the hierarchical structure by which those topics are related. This contextual-hierarchical perspective should, in turn, improve the accuracy of plagiarism detection. In addition, by mining the implicit relationships between hierarchical feature terms, our method can detect plagiarized documents with similar themes but using different topic words: a potential boon to plagiarism detection recall. In an experiment conducted on a dataset from Chinese paper database CNKI, the semantic-measure method indeed demonstrates accuracy and recall superior to those achieved with current state-of-the-art methods.

Suggested Citation

  • Tingting Zhang & Baozhen Lee & Qinghua Zhu, 2019. "Semantic measure of plagiarism using a hierarchical graph model," Scientometrics, Springer;Akadémiai Kiadó, vol. 121(1), pages 209-239, October.
    • Handle: RePEc:spr:scient:v:121:y:2019:i:1:d:10.1007_s11192-019-03204-x
    DOI: 10.1007/s11192-019-03204-x
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    References listed on IDEAS

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    1. K Vani & Deepa Gupta, 2018. "Integrating syntax‐semantic‐based text analysis with structural and citation information for scientific plagiarism detection," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 69(11), pages 1330-1345, November.
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