Multi-Agents Indexing System (MAIS) for Plagiarism Detection

With the extensive availability of technological systems all over the world and the increasing diffusion of information and documents by users, especially for the Arabic population, the development of semantic plagiarism detection systems has become essential due to its importance for protecting the rights of authors. Developing such a system that can covers the content of all Arabic documents using ontology as a semantic resource would be a complex and time-consuming task and would require intelligent natural language-processing capabilities. In the context of Arabic plagiarism detection systems using an Arabic ontology and permitting these systems to support semantic representation to more efficiently verify the originality of the research and meet the needs of researchers, this paper presents a novel approach for addressing plagiarism named Multi-agents Indexing System. The proposed system is composed of three phases: (1) natural language processing phase, (2) indexing phase and (3) evaluation phase. Our experimentations are based on the training dataset released for the AraPlagDet curpus. The obtained results indicated that the proposed system has improved the performance of plagiarism detection in Arabic documents with semantic indexing and mutli-agents system. (C) 2020 The Authors. Published by Elsevier B.V. on behalf of King Saud University.

Automated summary

This paper proposes a novel approach called Multi-agents Indexing System to address plagiarism in Arabic documents. The system consists of three phases: natural language processing, indexing, and evaluation. The results show that the proposed system improves the performance of plagiarism detection in Arabic documents with semantic indexing and multi-agents system.