@inproceedings{bu-etal-2025-query,
title = "Query-Driven Multimodal {G}raph{RAG}: Dynamic Local Knowledge Graph Construction for Online Reasoning",
author = "Bu, Chenyang and
Chang, Guojie and
Chen, Zihao and
Dang, CunYuan and
Wu, Zhize and
He, Yi and
Wu, Xindong",
editor = "Che, Wanxiang and
Nabende, Joyce and
Shutova, Ekaterina and
Pilehvar, Mohammad Taher",
booktitle = "Findings of the Association for Computational Linguistics: ACL 2025",
month = jul,
year = "2025",
address = "Vienna, Austria",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.findings-acl.1100/",
doi = "10.18653/v1/2025.findings-acl.1100",
pages = "21360--21380",
ISBN = "979-8-89176-256-5",
abstract = "An increasing adoption of Large Language Models (LLMs) in complex reasoning tasks necessitates their interpretability and reliability. Recent advances to that end include retrieval-augmented generation (RAG) and knowledge graph-enhanced RAG (GraphRAG), whereas they are constrained by static knowledge bases and ineffective multimodal data integration. In response, we propose a Query-Driven Multimodal GraphRAG framework that dynamically constructs local knowledge graphs tailored to query semantics. Our approach 1) derives graph patterns from query semantics to guide knowledge extraction, 2) employs a multi-path retrieval strategy to pinpoint core knowledge, and 3) supplements missing multimodal information ad hoc. Experimental results on the MultimodalQA and WebQA datasets demonstrate that our framework achieves the state-of-the-art performance among unsupervised competitors, particularly excelling in cross-modal understanding of complex queries."
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%0 Conference Proceedings
%T Query-Driven Multimodal GraphRAG: Dynamic Local Knowledge Graph Construction for Online Reasoning
%A Bu, Chenyang
%A Chang, Guojie
%A Chen, Zihao
%A Dang, CunYuan
%A Wu, Zhize
%A He, Yi
%A Wu, Xindong
%Y Che, Wanxiang
%Y Nabende, Joyce
%Y Shutova, Ekaterina
%Y Pilehvar, Mohammad Taher
%S Findings of the Association for Computational Linguistics: ACL 2025
%D 2025
%8 July
%I Association for Computational Linguistics
%C Vienna, Austria
%@ 979-8-89176-256-5
%F bu-etal-2025-query
%X An increasing adoption of Large Language Models (LLMs) in complex reasoning tasks necessitates their interpretability and reliability. Recent advances to that end include retrieval-augmented generation (RAG) and knowledge graph-enhanced RAG (GraphRAG), whereas they are constrained by static knowledge bases and ineffective multimodal data integration. In response, we propose a Query-Driven Multimodal GraphRAG framework that dynamically constructs local knowledge graphs tailored to query semantics. Our approach 1) derives graph patterns from query semantics to guide knowledge extraction, 2) employs a multi-path retrieval strategy to pinpoint core knowledge, and 3) supplements missing multimodal information ad hoc. Experimental results on the MultimodalQA and WebQA datasets demonstrate that our framework achieves the state-of-the-art performance among unsupervised competitors, particularly excelling in cross-modal understanding of complex queries.
%R 10.18653/v1/2025.findings-acl.1100
%U https://aclanthology.org/2025.findings-acl.1100/
%U https://doi.org/10.18653/v1/2025.findings-acl.1100
%P 21360-21380
Markdown (Informal)
[Query-Driven Multimodal GraphRAG: Dynamic Local Knowledge Graph Construction for Online Reasoning](https://aclanthology.org/2025.findings-acl.1100/) (Bu et al., Findings 2025)
ACL