A knowledge-based semantic framework for query expansion. Issue 5 (September 2019)

• Record Type: Journal Article
• Authors: Nasir, Jamal Abdul; Varlamis, Iraklis; Ishfaq, Samreen
• Is Part Of: Information processing & management. Volume 56:Issue 5(2019:Sep.)
• Page Start: 1605
• Page End: 1617
• Publication Date: 2019-09
• Subjects: Query expansion -- Semantic relatedness -- Relevance feedback -- Text similarity -- Search engine -- Semantic relevance feedback; Information storage and retrieval systems -- Periodicals; Information science -- Periodicals; Systèmes d'information -- Périodiques; Sciences de l'information -- Périodiques; Information science; Information storage and retrieval systems; Periodicals; 658.4038
• DOI: 10.1016/j.ipm.2019.04.007
• Languages: English
• ISSNs: 0306-4573

Abstract

Abstract: Searching for relevant material that satisfies the information need of a user, within a large document collection is a critical activity for web search engines. Query Expansion techniques are widely used by search engines for the disambiguation of user's information need and for improving the information retrieval (IR) performance. Knowledge-based, corpus-based and relevance feedback, are the main QE techniques, that employ different approaches for expanding the user query with synonyms of the search terms (word synonymy) in order to bring more relevant documents and for filtering documents that contain search terms but with a different meaning (also known as word polysemy problem) than the user intended. This work, surveys existing query expansion techniques, highlights their strengths and limitations and introduces a new method that combines the power of knowledge-based or corpus-based techniques with that of relevance feedback. Experimental evaluation on three information retrieval benchmark datasets shows that the application of knowledge or corpus-based query expansion techniques on the results of the relevance feedback step improves the information retrieval performance, with knowledge-based techniques providing significantly better results than their simple relevance feedback alternatives in all sets.