@inproceedings{729f835027654999944cbc6df55a17ed,
title = "ThoughtForest-KGQA: A Multi-Chain Tree Search for Knowledge Graph Reasoning",
abstract = "Most multi-hop Knowledge Graph Question Answering (KGQA) methods utilize fixed pruning strategies that, while efficient, critically impair the diversity of answer paths and fail to discover complex or less common correct answers. To address these limitations, this paper introduces ThoughtForest-KGQA, a novel multi-chain tree search algorithm. The method employs a dual-level reinforcement learning framework where a local-level agent optimizes individual reasoning chains by capturing fine-grained semantic details in the knowledge graph. Concurrently, a global-level agent strategically coordinates the simultaneous exploration of multiple chains. Comprehensive evaluations conducted across two distinct KGQA benchmarks reveal that this approach identifies a broader spectrum of correct answers, setting a new state-of-the-art in the field.",
keywords = "knowledge graph, multi-hop reasoning, reinforcement learning.",
author = "Xingrun Quan and Yongkang Zhou and Junjie Yao",
note = "Publisher Copyright: {\textcopyright} 2025 ACM. ; 34th ACM International Conference on Information and Knowledge Management, CIKM 2025 ; Conference date: 10-11-2025 Through 14-11-2025",
year = "2025",
month = nov,
day = "10",
doi = "10.1145/3746252.3760798",
language = "英语",
series = "CIKM 2025 - Proceedings of the 34th ACM International Conference on Information and Knowledge Management",
publisher = "Association for Computing Machinery, Inc",
pages = "5156--5160",
booktitle = "CIKM 2025 - Proceedings of the 34th ACM International Conference on Information and Knowledge Management",
}