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This paper examines the capacity of LLMs to reason with knowledge graphsusing their internal knowledge graph, i.e., the knowledge graph they learnedduring pre-training. Two research questions are formulated to investigate theaccuracy of LLMs in recalling information from pre-training knowledge graphsand their ability to infer knowledge graph relations from context. To addressthese questions, we employ LLMs to perform four distinct knowledge graphreasoning tasks. Furthermore, we identify two types of hallucinations that mayoccur during knowledge reasoning with LLMs: content and ontology hallucination.Our experimental results demonstrate that LLMs can successfully tackle bothsimple and complex knowledge graph reasoning tasks from their own memory, aswell as infer from input context.

On Exploring the Reasoning Capability of Large Language Models with Knowledge Graphs