Analyzing Semantic Concept Patterns to Detect Academic Plagiarism

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2017
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Proceedings of the 6th International Workshop on Mining Scientific Publications - WOSP 2017. - New York, USA : ACM Press, 2017. - pp. 46-53. - ISBN 978-1-4503-5388-5
Abstract
Detecting academic plagiarism is a pressing problem, e.g., for educational and research institutions, funding agencies, and academic publishers. Existing plagiarism detection systems reliably identify copied text, or near copies of text, but often fail to detect disguised forms of academic plagiarism, such as paraphrases, translations, and idea plagiarism. We present Semantic Concept Pattern Analysis - an approach that performs an integrated analysis of semantic text relatedness and structural text similarity. Using 25 officially retracted academic plagiarism cases, we demonstrate that our approach can detect plagiarism that established text matching approaches would not identify. We view our approach as a promising addition to improve the detection capabilities for strong paraphrases. We plan to further improve Semantic Concept Pattern Analysis and include the approach as part of an integrated detection process that analyzes heterogeneous similarity features to better identify the many possible forms of plagiarism in academic documents.
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Subject (DDC)
004 Computer Science
Keywords
Plagiarism Detection, Explicit Semantic Analysis
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6th International Workshop on Mining Scientific Publications WSOP 2017, Dec 15, 2017 - Dec 15, 2017, Toronto, Canada
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Cite This
ISO 690MEUSCHKE, Norman, Nicolas SIEBECK, Moritz SCHUBOTZ, Bela GIPP, 2017. Analyzing Semantic Concept Patterns to Detect Academic Plagiarism. 6th International Workshop on Mining Scientific Publications WSOP 2017. Toronto, Canada, Dec 15, 2017 - Dec 15, 2017. In: Proceedings of the 6th International Workshop on Mining Scientific Publications - WOSP 2017. New York, USA:ACM Press, pp. 46-53. ISBN 978-1-4503-5388-5. Available under: doi: 10.1145/3127526.3127535
BibTex
@inproceedings{Meuschke2017Analy-41874,
  year={2017},
  doi={10.1145/3127526.3127535},
  title={Analyzing Semantic Concept Patterns to Detect Academic Plagiarism},
  isbn={978-1-4503-5388-5},
  publisher={ACM Press},
  address={New York, USA},
  booktitle={Proceedings of the 6th International Workshop on Mining Scientific Publications  - WOSP 2017},
  pages={46--53},
  author={Meuschke, Norman and Siebeck, Nicolas and Schubotz, Moritz and Gipp, Bela}
}
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