EventRAG: Enhancing LLM Generation with Event Knowledge Graphs
Zairun Yang, Yilin Wang, Zhengyan Shi, Yuan Yao, Lei Liang, Keyan Ding, Emine Yilmaz, Huajun Chen
Abstract
Retrieval-augmented generation (RAG) systems often struggle with narrative-rich documents and event-centric reasoning, particularly when synthesizing information across multiple sources. We present EventRAG, a novel framework that enhances text generation through structured event representations. We first construct an Event Knowledge Graph by extracting events and merging semantically equivalent nodes across documents, while expanding under-connected relationships. We then employ an iterative retrieval and inference strategy that explicitly captures temporal dependencies and logical relationships across events. Experiments on UltraDomain and MultiHopRAG benchmarks show EventRAG’s superiority over baseline RAG systems, with substantial gains in generation effectiveness, logical consistency, and multi-hop reasoning accuracy. Our work advances RAG systems by integrating structured event semantics with iterative inference, particularly benefiting scenarios requiring temporal and logical reasoning across documents.
BibTeX
@inproceedings{yang-etal-2025-eventrag,
title = "{E}vent{RAG}: Enhancing {LLM} Generation with Event Knowledge Graphs",
author = "Yang, Zairun and
Wang, Yilin and
Shi, Zhengyan and
Yao, Yuan and
Liang, Lei and
Ding, Keyan and
Yilmaz, Emine and
Chen, Huajun and
Zhang, Qiang",
editor = "Che, Wanxiang and
Nabende, Joyce and
Shutova, Ekaterina and
Pilehvar, Mohammad Taher",
booktitle = "Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
month = jul,
year = "2025",
address = "Vienna, Austria",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.acl-long.830/",
doi = "10.18653/v1/2025.acl-long.830",
pages = "16967--16979",
ISBN = "979-8-89176-251-0"
}