Analyzing Semantic Concept Patterns to Detect Academic Plagiarism. Meuschke, N., Siebeck, N., Schubotz, M., & Gipp, B. In Proceedings of the International Workshop on Mining Scientific Publications (WOSP) co-located with the ACM/IEEE Joint Conference on Digital Libraries (JCDL), pages 46–53, Toronto, Canada, June, 2017. IEEE Computer Society. Venue Rating: CORE A*
Analyzing Semantic Concept Patterns to Detect Academic Plagiarism [pdf]Paper  doi  abstract   bibtex   
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.

Downloads: 0