ThoughtForest-KGQA: A Multi-Chain Tree Search for Knowledge Graph Reasoning

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.