论文标题
表达：知识图完成的时空功能嵌入
ExpressivE: A Spatio-Functional Embedding For Knowledge Graph Completion
论文作者
论文摘要
知识图本质上是不完整的。因此，大量研究是针对知识图完成（KGC）的，即，从知识图（KG）中表示的信息中预测了丢失的三元组。 KG嵌入模型（KGES）为KGC带来了令人鼓舞的结果，但是任何当前的KGE都无法：（1）完全捕获重要的推理模式（例如组成），（2）共同捕获突出的模式（例如，层次结构和组成），以及（3）提供捕获模式的直觉解释。在这项工作中，我们提出了表达性的表达式，这是一种完全表达的空间功能性的KGE，可以同时解决所有这些挑战。表达式将成对的实体作为点和关系作为虚拟三重空间$ \ mathbb {r}^{2d} $中的超平行平行四边形。该模型设计不仅允许共同捕获一组丰富的推理模式，而且还可以通过超平行平行四边形的空间关系来显示任何受支持的推理模式，从而提供表达嵌入及其捕获模式的直观且一致的几何解释。对标准KGC基准测试的实验结果表明，表达式与最先进的KGE具有竞争力，甚至在WN18RR上的表现明显优于它们。
Knowledge graphs are inherently incomplete. Therefore substantial research has been directed toward knowledge graph completion (KGC), i.e., predicting missing triples from the information represented in the knowledge graph (KG). KG embedding models (KGEs) have yielded promising results for KGC, yet any current KGE is incapable of: (1) fully capturing vital inference patterns (e.g., composition), (2) capturing prominent patterns jointly (e.g., hierarchy and composition), and (3) providing an intuitive interpretation of captured patterns. In this work, we propose ExpressivE, a fully expressive spatio-functional KGE that solves all these challenges simultaneously. ExpressivE embeds pairs of entities as points and relations as hyper-parallelograms in the virtual triple space $\mathbb{R}^{2d}$. This model design allows ExpressivE not only to capture a rich set of inference patterns jointly but additionally to display any supported inference pattern through the spatial relation of hyper-parallelograms, offering an intuitive and consistent geometric interpretation of ExpressivE embeddings and their captured patterns. Experimental results on standard KGC benchmarks reveal that ExpressivE is competitive with state-of-the-art KGEs and even significantly outperforms them on WN18RR.
