融合实体邻域与关系上下文，提升知识图谱补全效果
Enhancing Knowledge Graph Completion with Entity Neighborhood and Relation Context
- 同时利用实体邻域和关系上下文增强语言模型输入
- 在三个数据集上性能优于或匹配现有最佳方法
- 采样策略优化上下文使用，兼顾效率与准确性
知识图谱补全（KGC）旨在推断知识图谱中的缺失信息以解决其固有的不完整性。传统基于结构的方法虽有效，但因需对所有实体进行密集嵌入学习并逐一评分，面临显著计算开销与可扩展性挑战。近期基于文本的方法如T5、BERT通过将知识图谱三元组转化为文本进行推理，缓解了这些问题。然而，这些方法常未能充分利用上下文信息，主要关注实体邻域，忽视关系上下文。为此，我们提出KGC-ERC框架，整合实体邻域与关系上下文，丰富生成式语言模型的输入，增强其推理能力。此外，引入一种采样策略，在输入令牌约束下高效选择相关上下文，优化上下文利用，可能提升模型性能。在Wikidata5M、Wiki27K和FB15K-237-N数据集上的实验表明，KGC-ERC在预测性能与可扩展性方面均优于或匹配当前最优基线。
原文摘要 · Abstract (English)
Knowledge Graph Completion (KGC) aims to infer missing information in Knowledge Graphs (KGs) to address their inherent incompleteness. Traditional structure-based KGC methods, while effective, face significant computational demands and scalability challenges due to the need for dense embedding learning and scoring all entities in the KG for each prediction. Recent text-based approaches using language models like T5 and BERT have mitigated these issues by converting KG triples into text for reasoning. However, they often fail to fully utilize contextual information, focusing mainly on the neighborhood of the entity and neglecting the context of the relation. To address this issue, we propose KGC-ERC, a framework that integrates both types of context to enrich the input of generative language models and enhance their reasoning capabilities. Additionally, we introduce a sampling strategy to effectively select relevant context within input token constraints, which optimizes the utilization of contextual information and potentially improves model performance. Experiments on the Wikidata5M, Wiki27K, and FB15K-237-N datasets show that KGC-ERC outperforms or matches state-of-the-art baselines in predictive performance and scalability.
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