Complex Logical Reasoning over Knowledge Graphs using Large Language Models

Nurendra Choudhary, Chandan K. Reddy

Keywords: logical reasoning, knowledge graphs, large language models, complex reasoning

TL;DR: The paper introduces LARK, a decoupled approach for reasoning over KS that combines search with logical query reasoning using graph algorithms and LLMs. LARK outperforms SOTA methods on benchmark datasets, paving the way for future research.

Abstract: 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.

Primary Area: learning on graphs and other geometries & topologies

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Submission Number: 6798