补全知识图谱中实体应有的关系集合，让机器更懂事物该有哪些关联。
Beyond Triplet Plausibility: Relation Set Completion in Knowledge Graphs
- 通过学习实体已有关系的潜在模式，推断其应具备但缺失的关系
- 在三个标准数据集上显著提升关系补全效果，优于传统链接预测方法
- 适合研究知识图谱补全、语义推理及智能问答系统的开发者
知识图谱（KG）以三元组形式组织现实世界知识，支撑众多下游应用。由于固有的不完整性，知识图谱补全（KGC）被广泛研究，通常被建模为三元组预测任务，以链接预测为主流范式。然而，这一范式仅关注三元组层面的信息缺失，忽视了实体与关系之间的语义兼容性缺失。为此，本文提出关系集合补全任务（RSC），作为链接预测的补充，旨在推理给定实体应具有的语义兼容关系。我们进一步提出关系集合嵌入模型（RelSetE），通过建模实体已观察关系间的潜在模式，推断缺失关系。为评估该模型，我们在标准知识图谱基准上构建了三个新数据集。大量实验表明，RelSetE能有效捕捉实体-关系兼容性模式，在推断实体缺失关系方面表现优异。代码与数据已公开。
原文摘要 · Abstract (English)
Knowledge graphs (KGs) organize real-world knowledge as triplets and underpin many downstream applications. Due to their inherent incompleteness, knowledge graph completion (KGC) is widely studied and is typically formulated as triplet prediction, with link prediction as the dominant paradigm. However, this formulation focuses on the incompleteness of triplet-wise information and overlooks the incompleteness of entity-relation compatibility information. To address this limitation, we introduce a relation set completion task (RSC), which complements the link prediction task and aims to reason about missing relations that are semantically compatible with a given entity. We further propose a Relation Set Embedding model (RelSetE), which models latent patterns among the observed relations of entities to infer missing ones. To evaluate RelSetE, we derive three benchmark datasets from standard KG benchmarks. Extensive experiments demonstrate that RelSetE effectively captures entity-relation compatibility patterns and performs favorably in inferring missing relations of entities. Code and data are publicly available.
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