Journal of Digital Information Management Samia Zouaoui, Khaled Rezeg 17 3 2019 https://doi.org/10.6025/jdim/2019/17/3/145-163 http://dline.info/fpaper/jdim/v17i3/jdimv17i3_4.pdf In recent years, Automatic Natural Language Processing (ANLP) for Arabic language has received a great amount of attention for the development of several applications such as question answering, information retrieval and translation, etc. However, there are a few automated applications using Semantic Web technologies for retrieving Arabic-language documents despite the high demand and need for this content. In addition, the Arabic language presents serious challenges to researchers and developers of NLP applications. These challenges are due to the complexity of the morphological, syntactic and semantic characteristics specific to the Arabic text, which requires the use of semantic resources such as ontology. In our work, we propose a new approach based on ontology and multi-agent systems to index and filter Arabic documents. Our proposal is composed of five layers, each layer contains several agents: (1) Lexical Layer; (2) Syntactic Layer; (3) Semantic Layer; (4) Indexing Layer; and GUI/Interface Layer. Our Arabic ontology is manually constructed on the basis of schemes and their semantics meanings. We use also combination of Arabic WordNet contents and Arabic VerbNet in the process of constructing the ontology. We use the semantic similarity to find the relevant documents according to the users queries. The aim of this paper is to study the effect of patterns in solving the problem of the semantic indexing system (SIS). The main objective is to improve the quality of the indexing process to ensure the accuracy of the information search of relevant documents based on us ers multiword queries, and also to reduce indexing and search time. Indeed, our experiments are conducted on the basis of the combination of two Arab corpus: OSAC and SemEval. We compared our results Ontological Approach Based on Multi-Agent System for Indexing and Filtering Arabic Documents with Lucene in- dex for the same data and, we found that our approach achieves much better results than the other.