Reasoning over knowledge graphs (KGs) is a challenging task that requires a deep understanding of the complex relationships between entities and the underlying logic of their relations. Current approaches rely on learning geometries to embed entities in vector space for logical query operations, but they suffer from subpar performance on complex queries and dataset-specific representations. In this paper, we propose a novel decoupled approach, Language-guided Abstract Reasoning over Knowledge graphs (LARK), that formulates complex KG reasoning as a combination of contextual KG search and logical query reasoning, to leverage the strengths of graph extraction algorithms and large language models (LLM), respectively. Our experiments demonstrate that the proposed approach outperforms state-of-the-art KG reasoning methods on standard benchmark datasets across several logical query constructs, with significant performance gain for queries of higher complexity. Furthermore, we show that the performance of our approach improves proportionally to the increase in size of the underlying LLM, enabling the integration of the latest advancements in LLMs for logical reasoning over KGs. Our work presents a new direction for addressing the challenges of complex KG reasoning and paves the way for future research in this area.

翻译：知识图谱推理是一项具有挑战性的任务，需要深入理解实体间的复杂关系及其内在逻辑。现有方法依赖学习几何结构将实体嵌入向量空间以执行逻辑查询操作，但在复杂查询上表现欠佳，且表征方式局限于特定数据集。本文提出一种新颖的解耦方法——语言引导的知识图谱抽象推理（LARK），将复杂知识图谱推理形式化为上下文知识图谱搜索与逻辑查询推理的组合，分别利用图抽取算法和大语言模型（LLM）的优势。实验表明，所提方法在标准基准数据集上的多种逻辑查询结构均优于现有最先进的知识图谱推理方法，尤其在更高复杂度的查询中取得了显著性能提升。此外，我们发现该方法性能随底层LLM规模扩大而成比例提升，从而可整合最新LLM进展用于知识图谱逻辑推理。本研究为应对复杂知识图谱推理挑战开辟了新方向，并为该领域的后续研究奠定了基础。