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,...
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| Main Author: | |
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| Format: | Thesis |
| Language: | English English English |
| Published: |
2021
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| Subjects: | |
| Online Access: | http://eprints.uthm.edu.my/4933/1/24p%20NUHU%20YUSUF.pdf http://eprints.uthm.edu.my/4933/2/NUHU%20YUSUF%20COPYRIGHT%20DECLARATION.pdf http://eprints.uthm.edu.my/4933/3/NUHU%20YUSUF%20WATERMARK.pdf |
| Tags: | Add Tag |
| Summary: | 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%. |
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