Abstract
In a hyper-relational knowledge graph (HKG), each fact is composed of a main triple associated with attribute-value qualifiers, which express additional factual knowledge. The hyper-relational knowledge graph completion (HKGC) task aims at inferring plausible missing links in a HKG. Most existing approaches to HKGC focus on enhancing the communication between qualifier pairs and main triples, while overlooking two important properties that emerge from the monotonicity of the hyper-relational graphs representation regime. Stage Reasoning allows for a two-step reasoning process, facilitating the integration of coarse-grained inference results derived solely from main triples and fine-grained inference results obtained from hyper-relational facts with qualifiers. In the initial stage, coarse-grained results provide an upper bound for correct predictions, which are subsequently refined in the fine-grained step. More generally, Qualifier Monotonicity implies that by attaching more qualifier pairs to a main triple, we may only narrow down the answer set, but never enlarge it. This paper proposes the HyperMono model for hyper-relational knowledge graph completion, which realizes stage reasoning and qualifier monotonicity. To implement qualifier monotonicity HyperMono resorts to cone embeddings. Experiments on three real-world datasets with three different scenario conditions demonstrate the strong performance of HyperMono when compared to the SoTA.
Abstract (translated)
在超关系知识图（HKG）中，每个事实由与属性值定语相关的主要三元组组成，这些定语表示附加事实知识。超关系知识图完成（HKGC）任务的目的是推断HKG中的可能缺失链接。几乎所有现有的HKGC方法都关注于增强定语对之间以及主要三元组之间的通信，而忽略了从超关系图表示范式的单调性产生的两个重要属性。阶段推理允许进行两次推理过程，促进仅从主要三元组获得粗粒度推理结果以及仅从具有定语的知识图获得细粒度推理结果的整合。在初始阶段，粗粒度结果提供正确预测的上限，然后在细粒度阶段进行进一步的优化。更一般地说，定语单调性意味着，将更多的定语与主要三元组相关联，我们只能缩小答案集，但永远不会扩大它。本文提出了超Mono模型，用于超关系知识图完成，实现了阶段推理和定语单调性。为了实现定语单调性，超Mono求助于锥体嵌入。在三个真实世界数据集上进行三个不同情景条件的实验，证明了超Mono与SoTA之间的强烈性能。
URL
https://arxiv.org/abs/2404.09848
