用结构编码与相似实体信息提升大模型知识图谱补全效果
LLM-Based Knowledge Graph Completion Combining Discrete Structural Coding with Similar Entity Information
- 用离散结构编码生成候选实体初始排序
- 利用结构相似实体信息优化排序，FB15k-237上MRR和Hits@10更优
- 适合关注知识图谱补全与大模型结合的研究者
知识图谱补全需要模型同时利用文本描述和关系结构。现有基于大模型的方法要么将知识图谱结构编码为离散标记，要么仅优化有限候选实体集，这两类方法长期分离研究。本文提出CoSC，融合离散结构编码与结构相似实体信息。具体而言，大模型首先从离散结构编码生成初始候选实体排序，随后利用与查询实体结构相似的其他实体信息对排序进行精炼。在FB15k-237数据集上的实验表明，CoSC在MRR和Hits@10指标上优于现有基线，同时在Hits@1上保持竞争力。
原文摘要 · Abstract (English)
Knowledge graph completion requires models to use both textual descriptions and relational structure. Existing LLM-based methods either encode KG structure as discrete tokens or refine a restricted set of candidate entities, and these two directions have largely been studied separately. We propose CoSC for LLM-based KGC, which combines discrete structural coding with similar entity information. Specifically, an LLM generates an initial candidate entity ranking from discrete structural codes, after which information from entities with structures similar to that of the query entity refines the ranking. Experiments on FB15k-237 show that CoSC outperforms existing baselines on MRR and Hits@10 while remaining competitive on Hits@1.
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