Fuzzy-Semantic Similarity for Automatic Multilingual Plagiarism Detection

Abstract

Abstract-A word may have multiple meanings or senses, it could be modeled by considering that words in a sentence have a fuzzy set that contains words with similar meaning, which make detecting plagiarism a hard task especially when dealing with semantic meaning, and even harder for cross language plagiarism detection. Arabic is known by its richness, word's constructions and meanings diversity, hence changing texts from/to Arabic is a complex task, and therefore adopting a fuzzy semantic-based approach seems to be the best solution. In this paper, we propose a detailed fuzzy semantic-based similarity model for analyzing and comparing texts in CLP cases, in accordance with the WordNet lexical database, to detect plagiarism in documents translated from/to Arabic, a preprocessing phase is essential to form operable data for the fuzzy process. The proposed method was applied to two texts (Arabic/English), taking into consideration the specificities of the Arabic language. The result shows that the proposed method can detect 85% of the plagiarism cases.

Semantic measure of plagiarism using a hierarchical graph model

“…Fuzzy-semantic similarity technologies (Gupta et al 2014) can be used to detect text plagiarism in cases of rewording and restructuring. Ezzikouri et al (2017) propose a fuzzysemantic similarity model focused on the detection of obfuscated plagiarism, using a fuzzy membership function in conjunction with WordNet to calculate the degree of sentence similarity. Similarity values lie between 0 and 1: 0 for completely different sentences, 1 for duplicate sentences.…”