Multilingual Plagiarism Detection Using GNNs and Syntax-Semantic Knowledge Graphs

Abstract

Plagiarism has become an increasingly serious concern, especially within academia, driven by the unlimited availability of online information. It refers to the uncredited use of another author’s text, images, code, graphics, or ideas. There are several types of plagiarism as direct copying, paraphrasing, reformulating, or even translating across languages. Historically, approaches for Cross Language Plagiarism Detection (CLPD) are centered on linguistic and structural cues, multilingual alignment processes, or a measurement of perceived similarity based on concepts to compare. In this study, we propose a new hybrid Approach for CLPD, which combines semantic information from WordNet with the syntactic structure from Universal Dependencies, then these relations are modeled in knowledge graphs for multiple language pairs. In detail, we utilize RotatE for relation embedding, GCN for node embedding, and GAT for improvement in learning contextual representations, to build and embed the graphs. To prove the efficiency of the new hybrid method, three approaches are compared: 1) using only WordNet to build the knowledge graph, 2) using only Universal Dependencies, and 3) the proposed hybrid approach. Our approach demonstrates clear improvements over state-of-the-art baselines across three language pairs: English-Spanish, English-French, English-Arabic, and English-German, on datasets including PAN11, JRC-Acquis, Europarl, Wikipedia, OPUS, and a curated set of conference papers. The proposed hybrid approach achieves Plagdet scores of 98.50% for English-Spanish, 89.02% for English-German, 77.65% for English-Arabic, and 77.67% for English-French.