Detecting Cross-Language Plagiarism using Open Knowledge Graphs

• URL: http://arxiv.org/abs/2111.09749v1
• Date: Thu, 18 Nov 2021 15:23:27 GMT
• Title: Detecting Cross-Language Plagiarism using Open Knowledge Graphs
• Authors: Johannes Stegm\"uller, Fabian Bauer-Marquart, Norman Meuschke, Terry Ruas, Moritz Schubotz, Bela Gipp
• Abstract summary: We introduce the new multilingual retrieval model Cross-Language Ontology-Based Similarity Analysis. CL-OSA represents documents as entity vectors obtained from the open knowledge graph Wikidata. It reliably disambiguates homonyms and scales to allow its application to Web-scale document collections.
• Score: 7.378348990383349
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Identifying cross-language plagiarism is challenging, especially for distant language pairs and sense-for-sense translations. We introduce the new multilingual retrieval model Cross-Language Ontology-Based Similarity Analysis (CL\nobreakdash-OSA) for this task. CL-OSA represents documents as entity vectors obtained from the open knowledge graph Wikidata. Opposed to other methods, CL\nobreakdash-OSA does not require computationally expensive machine translation, nor pre-training using comparable or parallel corpora. It reliably disambiguates homonyms and scales to allow its application to Web-scale document collections. We show that CL-OSA outperforms state-of-the-art methods for retrieving candidate documents from five large, topically diverse test corpora that include distant language pairs like Japanese-English. For identifying cross-language plagiarism at the character level, CL-OSA primarily improves the detection of sense-for-sense translations. For these challenging cases, CL-OSA's performance in terms of the well-established PlagDet score exceeds that of the best competitor by more than factor two. The code and data of our study are openly available.

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