Query Expansion Techniques for Information Retrieval: a Survey

By Dr. Hiteshwar Kumar Azad, Akshay Deepak. Information Processing & Management, 2017

334 citations recorded by the source corpus.

Abstract With the ever increasing size of the web, relevant information extraction on the Internet with a query formed by a few keywords has become a big challenge. Query Expansion (QE) plays a crucial role in improving searches on the Internet. Here, the user’s initial query is reformulated by adding additional meaningful terms with similar significance. QE – as part of information retrieval (IR) – has long attracted researchers’ attention. It has become very influential in the field of personalized social document, question answering, cross-language IR, information filtering and multimedia IR. Research in QE has gained further prominence because of IR dedicated conferences such as TREC (Text Information Retrieval Conference) and CLEF (Conference and Labs of the Evaluation Forum). This paper surveys QE techniques in IR from 1960 to 2017 with respect to core techniques, data sources used, weighting and ranking methodologies, user participation and applications – bringing out similarities and differences.

Subfields: Artificial Intelligence, Information Systems

View Paper: https://www.semanticscholar.org/paper/9130ed011369ee8c4853b562309289488fb5949a

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Citations in the corpus

Large Language Models for Information Retrieval: A Survey (2023, neutral): “Language models (LMs) are designed to calculate the generative likelihood of word sequences by taking into account the contextual information from preceding words, thereby predicting the probability of subsequent words.”
“Chatting with ChatGPT”: Analyzing the factors influencing users' intention to Use the Open AI's ChatGPT using the UTAUT model (2023, background): “Prior studies [118,119] have found that incorporating query expansion techniques, which involve expanding the original search query to include related terms, could significantly improve the effectiveness of information retrieval systems.”
Answering Science Exam Questions Using Query Rewriting with Background Knowledge (2018, background): “Query expansion and reformulation – particularly in the area of information retrieval (IR) – is well studied (Azad and Deepak 2017).”
Deep Literature Survey Automation with an Iterative Workflow (2025, unknown): “First, retrieval can be imprecise and static due to reliance on a short topic description (often just a few tokens) as the retrieval query (Sun et al., 2019; Azad & Deepak, 2019; Wang et al., 2020).”
Retrieval And Structuring Augmented Generation with Large Language Models (2025, unknown): “Query expansion [7, 19, 178, 213] and rewriting [143, 159, 277] aim to improve retrieval by refor-mulating the input query.”