用大模型生成规则，再用逻辑推理筛选，提升知识库补全的准确性和多样性。
Large Language Model-Enhanced Symbolic Reasoning for Knowledge Base Completion
- 用大模型从知识图谱子图中生成多样规则，增强推理多样性。
- 通过规则推理器筛选关键规则，有效抑制大模型幻觉问题。
- 在多个数据集上表现优异，适合需要高可靠性的知识推理场景。
将大语言模型（LLMs）与基于规则的推理相结合，可显著提升知识库补全（KBC）的灵活性与可靠性。传统基于规则的方法具备可验证性但灵活性不足，而大语言模型虽具强大语义理解能力，却易产生幻觉。为此，我们提出一种新框架，包含子图提取器、大模型规则生成器和规则推理器。子图提取器首先从知识库中采样子图；大模型基于这些子图生成多样且有意义的候选规则；规则推理器进一步对生成的规则进行精炼，筛选出对补全任务最有价值的关键规则。该方法既利用了大模型的丰富语义理解能力，又通过规则推理保障了结果的逻辑严谨性。实验表明，该框架在多个知识库数据集上均表现出色，展现了良好的鲁棒性与泛化能力。
原文摘要 · Abstract (English)
Integrating large language models (LLMs) with rule-based reasoning offers a powerful solution for improving the flexibility and reliability of Knowledge Base Completion (KBC). Traditional rule-based KBC methods offer verifiable reasoning yet lack flexibility, while LLMs provide strong semantic understanding yet suffer from hallucinations. With the aim of combining LLMs' understanding capability with the logical and rigor of rule-based approaches, we propose a novel framework consisting of a Subgraph Extractor, an LLM Proposer, and a Rule Reasoner. The Subgraph Extractor first samples subgraphs from the KB. Then, the LLM uses these subgraphs to propose diverse and meaningful rules that are helpful for inferring missing facts. To effectively avoid hallucination in LLMs' generations, these proposed rules are further refined by a Rule Reasoner to pinpoint the most significant rules in the KB for Knowledge Base Completion. Our approach offers several key benefits: the utilization of LLMs to enhance the richness and diversity of the proposed rules and the integration with rule-based reasoning to improve reliability. Our method also demonstrates strong performance across diverse KB datasets, highlighting the robustness and generalizability of the proposed framework.
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