Semantic Boolean Arabic Information Retrieval
This work addresses the challenge of semantic retrieval for Arabic language users, but it appears incremental as it builds on existing IR methods.
The paper tackled the problem of Arabic Information Retrieval (AIR) by proposing a semantic Boolean IR framework to address weaknesses in handling semantics, achieving measured improvements in precision, recall, and run time compared to traditional models.
Arabic language is one of the most widely spoken languages. This language has a complex morphological structure and is considered as one of the most prolific languages in terms of article linguistic. Therefore, Arabic Information Retrieval (AIR) models need specific techniques to deal with this complex morphological structure. This paper aims to develop an integrate AIR frameworks. It lists and analysis the different Information Retrieval (IR) methods and techniques such as query processing, stemming and indexing which are used in AIR systems. We conclude that AIR frameworks have a weakness to deal with semantic in term of indexing, Boolean model, Latent Semantic Analysis (LSA), Latent Semantic Index (LSI) and semantic ranking. Therefore, semantic Boolean IR framework is proposed in this paper. This model is implemented and the precision, recall and run time are measured and compared with the traditional IR model.