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PEARL proposes a path-entity aligned relational learning framework that uses contextual subgraphs and LLM-guided retrieval for inductive knowledge graph completion, achieving state-of-the-art results on benchmarks.
Proposes DuPLeR, a dual-path LLM reasoning framework for multimodal few-shot knowledge graph completion, combining LLM-derived type priors with factual structures to improve inductive KGC under data scarcity.