Two-view knowledge graphs (KGs) jointly represent two components: an ontology
view for abstract and commonsense concepts, and an instance view for specific
entities that are instantiated from ontological concepts. As such, these KGs
contain heterogeneous structures that are hierarchical, from the ontology-view,
and cyclical, from the instance-view. Despite these various structures in KGs,
most recent works on embedding KGs assume that the entire KG belongs to only
one of the two views but not both simultaneously. For works that seek to put
both views of the KG together, the instance and ontology views are assumed to
belong to the same geometric space, such as all nodes embedded in the same
Euclidean space or non-Euclidean product space, an assumption no longer
reasonable for two-view KGs where different portions of the graph exhibit
different structures. To address this issue, we define and construct a
dual-geometric space embedding model (DGS) that models two-view KGs using a
complex non-Euclidean geometric space, by embedding different portions of the
KG in different geometric spaces. DGS utilizes the spherical space, hyperbolic
space, and their intersecting space in a unified framework for learning
embeddings. Furthermore, for the spherical space, we propose novel closed
spherical space operators that directly operate in the spherical space without
the need for mapping to an approximate tangent space. Experiments on public
datasets show that DGS significantly outperforms previous state-of-the-art
baseline models on KG completion tasks, demonstrating its ability to better
model heterogeneous structures in KGs.

本研究构建双视图知识图谱嵌入模型，使用复杂非欧几里德几何空间模拟两个视图，并使用球体空间、双曲空间及其交集进行统一框架学习。该模型在公共数据集上表现优异，表明其在模拟知识图谱的异构结构方面的能力更强。