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Semantic measure of plagiarism using a hierarchical graph model

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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.

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Acknowledgements

The author acknowledges the support by the Project No. 71673122 funded by National Natural Science Foundation of China and Nanjing University Innovation and Creative Program for PhD candidate CXCY17-09.

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Authors and Affiliations

  1. School of Engineering Management, Nanjing University, Nanjing, 210009, China

    Tingting Zhang & Qinghua Zhu

  2. School of Information Engineering, Nanjing Audit University, Nanjing, 210031, China

    Baozhen Lee

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  1. Tingting Zhang
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  2. Baozhen Lee
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Correspondence to Qinghua Zhu.

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Zhang, T., Lee, B. & Zhu, Q. Semantic measure of plagiarism using a hierarchical graph model. Scientometrics 121, 209–239 (2019). https://doi.org/10.1007/s11192-019-03204-x

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