# Query expansion techniques for information retrieval: A survey

arXiv: https://arxiv.org/abs/1708.00247 · HTML: https://mlsanity.com/paper/1708.00247/

## Key facts

- Cited by 44 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 1 Aug 2017.

## Built on by (most cited first, 30 of 44)

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- [MILL: Mutual Verification with Large Language Models for Zero-Shot Query Expansion](https://mlsanity.com/paper/2310.19056/) (2023)
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- [Privacy Policy Question Answering Assistant: A Query-Guided Extractive Summarization Approach](https://mlsanity.com/paper/2109.14638/) (2021)
- [Database-Augmented Query Representation for Information Retrieval](https://mlsanity.com/paper/2406.16013/) (2024)
- [Hybrid Retrieval for Hallucination Mitigation in Large Language Models: A Comparative Analysis](https://mlsanity.com/paper/2504.05324/) (2025)
- [Composite Code Sparse Autoencoders for first stage retrieval](https://mlsanity.com/paper/2204.07023/) (2022)
- [Relevance Feedback with Brain Signals](https://mlsanity.com/paper/2312.05669/) (2023)
- [Beyond Item Dissimilarities: Diversifying by Intent in Recommender Systems](https://mlsanity.com/paper/2405.12327/) (2024)
- [Leveraging LLM-Assisted Query Understanding for Live Retrieval-Augmented Generation](https://mlsanity.com/paper/2506.21384/) (2025)
- [Robust Sentiment Analysis for Low Resource languages Using Data Augmentation Approaches: A Case Study in Marathi](https://mlsanity.com/paper/2310.00734/) (2023)
- [GQE-PRF: Generative Query Expansion with Pseudo-Relevance Feedback](https://mlsanity.com/paper/2108.06010/) (2021)
- [Modified Query Expansion Through Generative Adversarial Networks for Information Extraction in E-Commerce](https://mlsanity.com/paper/2301.00036/) (2022)
- [Keyword Decisions in Sponsored Search Advertising: A Literature Review and Research Agenda](https://mlsanity.com/paper/2302.12372/) (2023)
- [Aggregated Knowledge Model: Enhancing Domain-Specific QA with Fine-Tuned and Retrieval-Augmented Generation Models](https://mlsanity.com/paper/2410.18344/) (2024)
- [A Survey of Query Optimization in Large Language Models](https://mlsanity.com/paper/2412.17558/) (2024)
- [Single LLM, Multiple Roles: A Unified Retrieval-Augmented Generation Framework Using Role-Specific Token Optimization](https://mlsanity.com/paper/2505.15444/) (2025)
- [KG-CQR: Leveraging Structured Relation Representations in Knowledge Graphs for Contextual Query Retrieval](https://mlsanity.com/paper/2508.20417/) (2025)
- [Beyond Case Law: Evaluating Structure-Aware Retrieval and Safety in Statute-Centric Legal QA](https://mlsanity.com/paper/2604.06173/) (2026)
- [Rethinking RAG in Long Videos: What to Retrieve and How to Use It?](https://mlsanity.com/paper/2606.13141/) (2026)
- [Q4EDA: A Novel Strategy for Textual Information Retrieval Based on User Interactions with Visual Representations of Time Series](https://mlsanity.com/paper/2101.08655/) (2021)
- [A Comparison of Approaches for Imbalanced Classification Problems in the Context of Retrieving Relevant Documents for an Analysis](https://mlsanity.com/paper/2205.01600/) (2022)

## Abstract (arXiv)

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.