LeSR uses an LLM to propose logic rules from sampled subgraphs, then a trainable reasoner weights those rules against the knowledge base and combines them with RotatE for KBC.
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Large Language Model-Enhanced Symbolic Reasoning for Knowledge Base Completion
LeSR uses an LLM to propose logic rules from sampled subgraphs, then a trainable reasoner weights those rules against the knowledge base and combines them with RotatE for KBC.