  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.
