IJITCS Vol. 15, No. 1, 8 Feb. 2023
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Search Engine, Query Expansion, Relevance Feedback, Information Retrieval, WWQE Model
Information systems have come a long way in the 21st century, with search engines emerging as the most popular and well-known retrieval systems. Several techniques have been used by researchers to improve the retrieval of relevant results from search engines. One of the approaches employed for improving relevant feedback of a retrieval system is Query Expansion (QE). The challenge associated with this technique is how to select the most relevant terms for the expansion. In this research work, we propose a query expansion technique based on Azak & Deepak's WWQE model. Our extended WWQE technique adopts Candidate Expansion Terms selection with the use of in-links and out-links. The top two relevant Wikipedia articles from the user's initial search were found using a custom search engine over Wikipedia. Following that, we ranked further Wikipedia articles that are semantically connected to the top two Wikipedia articles based on cosine similarity using TF-IDF Vectorizer. The expansion terms were then taken from the top 5 document titles. The results of the evaluation of our methodology utilizing TREC query topics (126-175) revealed that the system with extended features gave ranked results that were 11% better than those from the system with unexpanded queries.
Stephen Akuma, Promise Anendah, "A New Query Expansion Approach for Improving Web Search Ranking", International Journal of Information Technology and Computer Science(IJITCS), Vol.15, No.1, pp.42-55, 2023. DOI:10.5815/ijitcs.2023.01.05
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