Abstract
Information Retrieval (IR) is a field of computer science that deals with storing, searching, and retrievingdocuments that satisfy the user need. The modern standard Arabic language is rich in multiple meanings (senses) for manywords and this is substantially due to lack of diacritical marks. The task for finding appropriate meanings is a key demand inmost of the Arabic IR applications. Actually, the successful system should not be interested only in the retrieval quality andoblivious to the system efficiency. Thus, this paper contributes to improve the system effectiveness by finding appropriatestemming methodology, word sense disambiguation, and query expansion for addressing the retrieval quality of AIR. Also, itcontributes to improve the system efficiency through using a powerful metaheuristic search called Harmony Search (HS)algorithm inspired from the musical improvisation processes. The performance of the proposed system outperforms the one inthe traditional system in a rate of 19.5% while reduces the latency in an approximate rate of 0.077 second for each query.
Highlights
Information retrieval (IR) is a field of computer science that deals with storing, searching, and retrieving documents that can be texts, Web pages, images, and videos
We proposed a combination of ISRI light and heavy stemmers depending on the presence of the word in the Arabic WordNet (AWN)4
The databases used in the proposed and traditional systems are stored in separate places within the Redis server include the Inverted-Index, vector space model (VSM), and
Summary
Information retrieval (IR) is a field of computer science that deals with storing, searching, and retrieving documents that can be texts, Web pages, images, and videos. The proposed AIR system is tested on Arabic corpus called Zad-Al-Ma‟ad, namely ZAD in short, has 2730 Arabic documents, 25 Arabic queries, and supported by relevance judgments. It is written by the Islamic scholar “Ibn Al-Qyyim” [5],[6]. The essential contribution of this paper lies in improving AIR performance in terms of effectiveness through including the automatic word sense disambiguation and query expansion and in terms of efficiency through using a stochastic search rather than the traditional complete search approach
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