面向知识库补全的大语言模型增强符号推理
Large Language Model-Enhanced Symbolic Reasoning for Knowledge Base Completion
AI总结：
本文提出一种结合子图提取器、LLM提议器和规则推理器的框架，利用大语言模型生成多样规则并通过规则推理器精炼，以提升知识库补全的可靠性和灵活性。
摘要：
将大语言模型（LLMs）与基于规则的推理相结合，为提升知识库补全（KBC）的灵活性和可靠性提供了一种强有力的解决方案。传统的基于规则的KBC方法提供可验证的推理但缺乏灵活性，而LLMs具备强大的语义理解能力却存在幻觉问题。为了将LLMs的理解能力与基于规则方法的逻辑性和严谨性相结合，我们提出了一种新颖的框架，该框架由子图提取器（Subgraph Extractor）、LLM提议器（LLM Proposer）和规则推理器（Rule Reasoner）组成。子图提取器首先从知识库中采样子图。然后，LLM利用这些子图提出多样且有意义的规则，这些规则有助于推断缺失事实。为有效避免LLM生成内容中的幻觉，这些提出的规则会进一步由规则推理器进行精炼，以在知识库中定位对知识库补全最重要的规则。我们的方法具有若干关键优势：利用LLMs增强所提规则的丰富性和多样性，以及与基于规则的推理相结合以提高可靠性。我们的方法在多个不同的知识库数据集上展现出强大的性能，凸显了所提框架的鲁棒性和泛化能力。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
