@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://preview.aclanthology.org/ingestion-acl-25/2025.acl-long.830/",
    pages = "16967--16979",
    ISBN = "979-8-89176-251-0",
    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."
}