Ontological Approach Based on Multi-Agent System for Indexing and Filtering Arabic Documents
Samia Zouaoui, Khaled Rezeg
Journal of Digital Information Management, Vol. 17, No. 3, 2019.
DOI:  https://doi.org/10.6025/jdim/2019/17/3/145-163
Link: http://dline.info/fpaper/jdim/v17i3/jdimv17i3_4.pdf
Abstract:
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.