@inproceedings{xia-etal-2025-knowledge,
title = "Knowledge-Aware Query Expansion with Large Language Models for Textual and Relational Retrieval",
author = "Xia, Yu and
Wu, Junda and
Kim, Sungchul and
Yu, Tong and
Rossi, Ryan A. and
Wang, Haoliang and
McAuley, Julian",
editor = "Chiruzzo, Luis and
Ritter, Alan and
Wang, Lu",
booktitle = "Proceedings of the 2025 Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers)",
month = apr,
year = "2025",
address = "Albuquerque, New Mexico",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.naacl-long.216/",
doi = "10.18653/v1/2025.naacl-long.216",
pages = "4275--4286",
ISBN = "979-8-89176-189-6",
abstract = "Large language models (LLMs) have been used to generate query expansions augmenting original queries for improving information search. Recent studies also explore providing LLMs with initial retrieval results to generate query expansions more grounded to document corpus. However, these methods mostly focus on enhancing textual similarities between search queries and target documents, overlooking document relations. For queries like ``Find me a highly rated camera for wildlife photography compatible with my Nikon F-Mount lenses'', existing methods may generate expansions that are semantically similar but structurally unrelated to user intents. To handle such semi-structured queries with both textual and relational requirements, in this paper we propose a knowledge-aware query expansion framework, augmenting LLMs with structured document relations from knowledge graph (KG). To further address the limitation of entity-based scoring in existing KG-based methods, we leverage document texts as rich KG node representations and use document-based relation filtering for our Knowledge-Aware Retrieval (KAR). Extensive experiments on three datasets of diverse domains show the advantages of our method compared against state-of-the-art baselines on textual and relational semi-structured retrieval."
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<abstract>Large language models (LLMs) have been used to generate query expansions augmenting original queries for improving information search. Recent studies also explore providing LLMs with initial retrieval results to generate query expansions more grounded to document corpus. However, these methods mostly focus on enhancing textual similarities between search queries and target documents, overlooking document relations. For queries like “Find me a highly rated camera for wildlife photography compatible with my Nikon F-Mount lenses”, existing methods may generate expansions that are semantically similar but structurally unrelated to user intents. To handle such semi-structured queries with both textual and relational requirements, in this paper we propose a knowledge-aware query expansion framework, augmenting LLMs with structured document relations from knowledge graph (KG). To further address the limitation of entity-based scoring in existing KG-based methods, we leverage document texts as rich KG node representations and use document-based relation filtering for our Knowledge-Aware Retrieval (KAR). Extensive experiments on three datasets of diverse domains show the advantages of our method compared against state-of-the-art baselines on textual and relational semi-structured retrieval.</abstract>
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%0 Conference Proceedings
%T Knowledge-Aware Query Expansion with Large Language Models for Textual and Relational Retrieval
%A Xia, Yu
%A Wu, Junda
%A Kim, Sungchul
%A Yu, Tong
%A Rossi, Ryan A.
%A Wang, Haoliang
%A McAuley, Julian
%Y Chiruzzo, Luis
%Y Ritter, Alan
%Y Wang, Lu
%S Proceedings of the 2025 Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers)
%D 2025
%8 April
%I Association for Computational Linguistics
%C Albuquerque, New Mexico
%@ 979-8-89176-189-6
%F xia-etal-2025-knowledge
%X Large language models (LLMs) have been used to generate query expansions augmenting original queries for improving information search. Recent studies also explore providing LLMs with initial retrieval results to generate query expansions more grounded to document corpus. However, these methods mostly focus on enhancing textual similarities between search queries and target documents, overlooking document relations. For queries like “Find me a highly rated camera for wildlife photography compatible with my Nikon F-Mount lenses”, existing methods may generate expansions that are semantically similar but structurally unrelated to user intents. To handle such semi-structured queries with both textual and relational requirements, in this paper we propose a knowledge-aware query expansion framework, augmenting LLMs with structured document relations from knowledge graph (KG). To further address the limitation of entity-based scoring in existing KG-based methods, we leverage document texts as rich KG node representations and use document-based relation filtering for our Knowledge-Aware Retrieval (KAR). Extensive experiments on three datasets of diverse domains show the advantages of our method compared against state-of-the-art baselines on textual and relational semi-structured retrieval.
%R 10.18653/v1/2025.naacl-long.216
%U https://aclanthology.org/2025.naacl-long.216/
%U https://doi.org/10.18653/v1/2025.naacl-long.216
%P 4275-4286
Markdown (Informal)
[Knowledge-Aware Query Expansion with Large Language Models for Textual and Relational Retrieval](https://aclanthology.org/2025.naacl-long.216/) (Xia et al., NAACL 2025)
ACL