用实体上下文增强知识图谱补全，提升长尾实体推理效果
Retrieval, Reasoning, Re-ranking: A Context-Enriched Framework for Knowledge Graph Completion
- 三阶段框架：检索相关三元组与上下文，大模型生成答案，再排序优选
- 在FB15k237和WN18RR上分别提升12.3%和5.6% Hits@1
- 适合处理关系噪声多、实体稀疏的知识图谱场景
知识图谱补全（KGC）旨在从不完整三元组中推断缺失实体。现有基于嵌入的方法仅依赖图谱三元组，易受虚假关系模式和长尾实体影响；而基于文本的方法又面临图谱三元组与自然语言间的语义鸿沟。除三元组外，实体上下文（如标签、描述、别名）对图谱增强具有重要意义。为此，我们提出KGR3，一种上下文增强型知识图谱补全框架。该框架包含三个模块：首先，检索模块从知识图谱中获取支持性三元组，从基础嵌入模型获取候选答案，并为相关实体检索上下文；其次，推理模块利用大语言模型生成每个查询三元组的潜在答案；最后，重排序模块融合两模块的候选答案，并微调大模型以输出最优结果。在多个主流数据集上的实验表明，KGR3持续提升各类KGC方法性能。其中，最佳变体在FB15k237和WN18RR数据集上分别实现12.3%和5.6%的Hits@1绝对提升。
原文摘要 · Abstract (English)
The Knowledge Graph Completion~(KGC) task aims to infer the missing entity from an incomplete triple. Existing embedding-based methods rely solely on triples in the KG, which is vulnerable to specious relation patterns and long-tail entities. On the other hand, text-based methods struggle with the semantic gap between KG triples and natural language. Apart from triples, entity contexts (e.g., labels, descriptions, aliases) also play a significant role in augmenting KGs. To address these limitations, we propose KGR3, a context-enriched framework for KGC. KGR3 is composed of three modules. Firstly, the Retrieval module gathers supporting triples from the KG, collects plausible candidate answers from a base embedding model, and retrieves context for each related entity. Then, the Reasoning module employs a large language model to generate potential answers for each query triple. Finally, the Re-ranking module combines candidate answers from the two modules mentioned above, and fine-tunes an LLM to provide the best answer. Extensive experiments on widely used datasets demonstrate that KGR3 consistently improves various KGC methods. Specifically, the best variant of KGR3 achieves absolute Hits@1 improvements of 12.3% and 5.6% on the FB15k237 and WN18RR datasets.
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