让关系嵌入学会利用实体的数值属性，提升知识图谱推理效果。
ReaLitE: Enrichment of Relation Embeddings in Knowledge Graphs using Numeric Literals
- 以关系为中心，动态融合实体数值属性到关系嵌入中
- 在链接预测和节点分类任务上超越现有最佳模型
- 适用于含数值属性的知识图谱，尤其适合数据不完整场景
多数知识图谱嵌入（KGE）方法专注于实体与关系，忽视了可能蕴含重要信息的数值型字面量。现有方法或直接将数值融入实体嵌入，或预处理时将其转为实体，导致信息损失；另一些方法依赖数值数据完整性，不适用于真实世界图谱。本文提出ReaLitE，一种面向关系的新型KGE模型，可动态聚合并融合实体的数值属性与连接关系的嵌入。该模型可与现有常规KGE方法结合，支持多种数值聚合方式，包括可学习机制。我们在多个链接预测与节点分类基准上进行了全面评估，结果表明ReaLitE在两项任务中均优于当前最优方法。
原文摘要 · Abstract (English)
Most knowledge graph embedding (KGE) methods tailored for link prediction focus on the entities and relations in the graph, giving little attention to other literal values, which might encode important information. Therefore, some literal-aware KGE models attempt to either integrate numerical values into the embeddings of the entities or convert these numerics into entities during preprocessing, leading to information loss. Other methods concerned with creating relation-specific numerical features assume completeness of numerical data, which does not apply to real-world graphs. In this work, we propose ReaLitE, a novel relation-centric KGE model that dynamically aggregates and merges entities' numerical attributes with the embeddings of the connecting relations. ReaLitE is designed to complement existing conventional KGE methods while supporting multiple variations for numerical aggregations, including a learnable method. We comprehensively evaluated the proposed relation-centric embedding using several benchmarks for link prediction and node classification tasks. The results showed the superiority of ReaLitE over the state of the art in both tasks.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。