@inproceedings{hao-wu-2025-fact,
    title = "Fact Verification on Knowledge Graph via Programmatic Graph Reasoning",
    author = "Hao, Yuanzhen  and
      Wu, Desheng",
    editor = "Christodoulopoulos, Christos  and
      Chakraborty, Tanmoy  and
      Rose, Carolyn  and
      Peng, Violet",
    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2025",
    month = nov,
    year = "2025",
    address = "Suzhou, China",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/author-page-yu-wang-polytechnic/2025.findings-emnlp.293/",
    doi = "10.18653/v1/2025.findings-emnlp.293",
    pages = "5480--5495",
    ISBN = "979-8-89176-335-7",
    abstract = "Fact verification on knowledge graphs (KGs) uses the structured representation of entities and relations as evidence for validating claims. Previous methods for KG-based fact verification predominantly use natural language inference (NLI) models to predict entailment between claims and KG triples, based on implicit reasoning. We propose Programmatic Graph Reasoning (PGR), a novel framework that integrates large language models (LLMs) for fact verification on KGs. PGR explicitly encodes the reasoning process as a graph reasoning program composed of predefined functions to verify claims step by step. These functions are executed sequentially for graph reasoning and final result prediction. By making the graph reasoning process explicit, PGR ensures more precise and transparent reasoning steps compared to implicit methods. Experimental results on the FactKG dataset demonstrate that PGR achieves state-of-the-art performance with 86.82{\%} accuracy, outperforming all the baseline models. Further analysis confirms the interpretability and effectiveness of our method in handling complex graph reasoning."
}