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Research questionHow can knowledge graph completion predict links for unseen entities while adapting path relevance to each query’s structural context?At test time, target entities were not observed during training, so systems must transfer relational and structural patterns rather than memorize entity-specific links. A path that helps one query may be misleading for another because its relevance depends on the surrounding graph.
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Latest papersRecent research connected to this question, newest first.PEARL: Path-Entity Aligned Relational Learning with Contextual Subgraphs for Inductive Knowledge Graph CompletionThe source studies inductive knowledge graph completion using contextual subgraphs and LLM-guided path retrieval, evaluated on WN18RR, FB15k-237, and NELL-995. Evidence includes Hits@10 results, ablations, efficiency analyses, and case studies; it does not establish performance beyond these benchmarks or deployment settings.research paper · Sep 2, 2026
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How much complementary knowledge can link-prediction models recover together in incomplete knowledge graphs?How can knowledge graph embeddings model valid inference patterns without over-generalizing from sparse evidence?How can entity-alignment models transfer to unseen heterogeneous knowledge graphs without retraining?How can uncertain knowledge graphs predict missing confidence scores without losing global structure or training stability?