论文标题
EL ++知识基础的Faithiful嵌入
Faithiful Embeddings for EL++ Knowledge Bases
论文作者
论文摘要
最近，越来越多的努力用于学习符号知识库（KB）的持续表示。但是，这些方法要么仅嵌入数据级知识（ABOX），要么在处理概念级知识（Tbox）时受到固有的局限性，即它们不能忠实地对KBS中存在的逻辑结构进行建模。我们提出了Boxel，这是一种几何KB嵌入方法，可以更好地捕获描述逻辑EL ++中的逻辑结构（即Abox和Tbox Axioms）。 Boxel模型在KB中作为轴平行的框，适用于建模概念相交的轴线框，作为点内部的实体以及概念/实体之间的关系作为仿射转换。我们显示了Boxel的理论保证（声音），以保存逻辑结构。也就是说，损坏0的Boxel嵌入模型是KB的（逻辑）模型。 （合理）补充推理和用于蛋白质蛋白预测的现实应用的实验结果表明，Boxel的表现优于传统知识图嵌入方法以及最先进的EL ++嵌入方法。
Recently, increasing efforts are put into learning continual representations for symbolic knowledge bases (KBs). However, these approaches either only embed the data-level knowledge (ABox) or suffer from inherent limitations when dealing with concept-level knowledge (TBox), i.e., they cannot faithfully model the logical structure present in the KBs. We present BoxEL, a geometric KB embedding approach that allows for better capturing the logical structure (i.e., ABox and TBox axioms) in the description logic EL++. BoxEL models concepts in a KB as axis-parallel boxes that are suitable for modeling concept intersection, entities as points inside boxes, and relations between concepts/entities as affine transformations. We show theoretical guarantees (soundness) of BoxEL for preserving logical structure. Namely, the learned model of BoxEL embedding with loss 0 is a (logical) model of the KB. Experimental results on (plausible) subsumption reasonings and a real-world application for protein-protein prediction show that BoxEL outperforms traditional knowledge graph embedding methods as well as state-of-the-art EL++ embedding approaches.
