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Complex Logical Reasoning over Knowledge Graphs using Large Language Models
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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.
Forward citations
Cited by 5 Pith papers
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In a small user study and two case studies, a hypothesis-chain workflow grounded in knowledge graphs helped biomedical researchers construct and validate hypotheses from machine-learning predictions more effectively t...
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Graph Counselor: Adaptive Graph Exploration via Multi-Agent Synergy to Enhance LLM Reasoning
A multi-agent GraphRAG framework with self-reflection improves LLM accuracy on knowledge graph question answering.
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Optimizing the Interface Between Knowledge Graphs and LLMs for Complex Reasoning
Hyperparameter tuning of Cognee's knowledge graph pipeline yields consistent but uneven gains across three multi-hop QA benchmarks, with best configurations varying by dataset and metric.
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MultiCNKG: Integrating Cognitive Neuroscience, Gene, and Disease Knowledge Graphs Using Large Language Models
An LLM merges three biomedical ontologies into a small knowledge graph, but its validation metrics are self-contradictory and the resource is not released.
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