Terms interrelationship query expansion to improve accuracy of Quran search

Quran retrieval system is becoming an instrument for users to search for needed information. The search engine is one of the most popular search engines that successfully implemented for searching relevant verses queries. However, a major challenge to the Quran search engine is word ambiguities, specifically lexical ambiguities. With the advent of query expansion techniques for Quran retrieval systems, the performance of the Quran retrieval system has problem and issue in terms of retrieving users needed information. The results of the current semantic techniques still lack precision values without considering several semantic dictionaries. Therefore, this study proposes a stemmed terms interrelationship query expansion approach to improve Quran search results. More specifically, related terms were collected from different semantic dictionaries and then utilize to get roots of words using a stemming algorithm. To assess the performance of the stemmed terms interrelationship query expansion, experiments were conducted using eight Quran datasets from the Tanzil website. Overall, the results indicate that the stemmed terms interrelationship query expansion is superior to unstemmed terms interrelationship query expansion in Mean Average Precision with Yusuf Ali 68%, Sarawar 67%, Arberry 72%, Malay 65%, Hausa 62%, Urdu 62%, Modern Arabic 60% and Classical Arabic 59%

Coupled intrinsic and extrinsic human language resource-based query expansion

Poor information retrieval performance has often been attributed to the query-document vocabulary mismatch problem which is defined as the difficulty for human users to formulate precise natural language queries that are in line with the vocabulary of the documents deemed relevant to a specific search goal. To alleviate this problem, query expansion processes are applied in order to spawn and integrate additional terms to an initial query. This requires accurate identification of main query concepts to ensure the intended search goal is duly emphasized and relevant expansion concepts are extracted and included in the enriched query. Natural language queries have intrinsic linguistic properties such as parts-of-speech labels and grammatical relations which can be utilized in determining the intended search goal. Additionally, extrinsic language-based resources such as ontologies are needed to suggest expansion concepts semantically coherent with the query content. We present here a query expansion framework which capitalizes on both linguistic characteristics of user queries and ontology resources for query constituent encoding, expansion concept extraction and concept weighting. A thorough empirical evaluation on real-world datasets validates our approach against unigram language model, relevance model and a sequential dependence-based technique