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 abstract 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（KG）上的推理是一项具有挑战性的任务，需要对实体间复杂关系及其关系背后的逻辑有深刻理解。现有方法依赖学习几何结构将实体嵌入向量空间以进行逻辑查询操作，但在复杂查询和数据集特定表示上性能欠佳。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），它将复杂KG推理建模为上下文KG搜索与抽象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（LLM）的优势。实验表明，所提方法在多个逻辑查询构造的标准基准数据集上优于最先进的KG推理方法，且对更高复杂度的查询具有显著性能提升。此外，我们证明模型性能随底层LLM规模增大而成比例提升，这使得能够整合LLM在KG逻辑推理中的最新进展。我们的工作为解决复杂KG推理的挑战提供了新方向，并为该领域的未来研究铺平了道路。