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：对知识图谱进行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关系的底层逻辑。当前方法主要依赖学习几何结构以将实体嵌入向量空间，从而执行逻辑查询操作，但这些方法在复杂查询上表现欠佳，且会产生特定于数据集的表征。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），该方法将复杂知识图谱推理分解为上下文知识图谱搜索与逻辑查询推理的组合，从而分别利用图提取算法和大型语言模型的优势。实验表明，所提方法在多个逻辑查询构型的标准基准数据集上均优于最先进的知识图谱推理方法，且对更高复杂度的查询性能提升显著。此外，我们发现方法性能与底层大型语言模型的规模成正比提升，从而能够集成大型语言模型在知识图谱逻辑推理领域的最新进展。本研究为应对复杂知识图谱推理挑战提供了新方向，并为该领域未来研究奠定了基础。