RISE: Enhancing Knowledge Graph Completion with Large Language Models via Rollback-Guided Influential Subgraphs

Knowledge graph completion (KGC) with large language models (LLMs) can benefit from query-specific structural context, yet two challenges remain: identifying structures that are truly relevant to a particular prediction and integrating them into LLMs without verbose graph descriptions. To address these challenges, we propose RISE, a Rollback-guided Influential Subgraph-Enhanced framework for KGC with LLMs. RISE traces triple-level knowledge graph embedding (KGE) optimization updates and uses rollback-induced prediction changes to prioritize query-candidate paths, which are aggregated into compact influential subgraphs. These subgraphs are then encoded and fused with candidate representations to form latent structural prefixes for LLM-based reranking, complementing global KGE representations and textual semantics with query-specific local structure. Experiments on four datasets show that RISE achieves the best performance on most evaluation metrics among the compared methods, while further analyses demonstrate the benefits of prediction-relevant subgraph selection and compact latent conditioning.