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An improved semantic query expansion approach using incremental user tag profile for efficient information retrieval

Author(s)
Muhammad Ahsan Raza Department of Information Technology, Bahauddin Zakariya University,Multan 60000,Pakistan
Muhammad Ali Department of Information Technology, Bahauddin Zakariya University,Multan 60000,Pakistan
Maruf Pasha Department of Information Technology, Bahauddin Zakariya University,Multan 60000,Pakistan
Mubashir Ali Department of Software Engineering, Lahore Garrison University,Lahore 54000,Pakistan
Abstract
The World Wide Web (WWW) comprises a wide range of information, and it is mainly operated on the principles of keyword matching which often reduces accurate information retrieval. The Keyword matching mechanism faces word mismatch problems while retrieving relevant information. Furthermore, the inherent ambiguity of short keyword queries demands enhanced methods for Web retrieval. Ontological-based query expansion is one of the primary methods for Web retrieval, and it handles the vocabulary mismatch problem by computing semantics from the ontology knowledgebase. However, the retrieval of information relevant to user interests is a major challenge. In this paper, we seek to improve retrieval performance by leveraging user preferences and ontology semantics in the process of query expansion. The expansion words are added to the user query using WordNet lexicon and domain ontology. Additionally, the search intent of the user is also added as expansion words by exploiting a tag-based user profile. When it comes to obtaining relevant documents, the proposed framework outperforms the keyword-based approach by achieving a 76% F1-score. This noticeable improvement accurately reflects the importance of including user intents in the process of semantic query expansion.
Publication Details
Page(s) 1-9
DOI DOI not available
Published Journal: VFAST Transactions on Software Engineering, Volume: 10, Issue: 3, Year: 2022

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Keywords
ontology, COVID-19, COVID-19, natural language processing, Entity recognition, Semantic query expansion
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