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.

本研究提出一种基于图形提取算法和大型语言模型的解耦合语言引导抽象推理方法，将复杂的知识图推理作为上下文知识图搜索和抽象逻辑查询推理的组合，显著提高了复杂查询的性能。