Query Expansion Method For Quran Search Using Semantic Search And Lucene Ranking

Search engines are becoming an instrument for users to search for needed information. The web search engine is one of the most popular search engines that are successfully implemented in many application areas. A major challenge to a web search engine is vocabulary mismatch, specifically term selection and weighting. With the advent of query expansion techniques, the performance of web search engines has been improved in terms of retrieving users needed information. These techniques add additional terms to a query for better search results. The results of these techniques still lack higher precision values. This paper proposed a new hybrid query expansion approach to improve Quran search results using semantic search and Lucene ranking. More specifically, a novel semantic search for Quran search is first presented, in which, Quran search queries are expanded with word synonyms and combined with Quran ontology to get the relationships between concepts within the expanded query. Based on the proposed semantic search, the Lucene ranking algorithm is adopted and modified with stemming, stop words and derivatives to improve the results of query expansion for Quran search. To assess the performance of the proposed query expansion, semantic search and Lucene ranking experiments were conducted using 8 Quran datasets from the Tanzil web site. Overall, the results indicate that the proposed LQA and SSLR on Arberry dataset is superior to other query expansion techniques for Quran search in MAP with 55% and 48% respectively. Future research work should focus on improving Quran ontology and utilizes within a distributed semantic representation