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。

双几何空间嵌入模型用于双视图知识图谱

Dual-Geometric Space Embedding Model for Two-View Knowledge Graphs

Many large-scale knowledge bases simultaneously represent two views of
knowledge graphs (KGs): an ontology view for abstract and commonsense concepts,
and an instance view for specific entities that are instantiated from
ontological concepts. Existing KG embedding models, however, merely focus on
representing one of the two views alone. In this paper, we propose a novel
two-view KG embedding model, JOIE, with the goal to produce better knowledge
embedding and enable new applications that rely on multi-view knowledge. JOIE
employs both cross-view and intra-view modeling that learn on multiple facets
of the knowledge base. The cross-view association model is learned to bridge
the embeddings of ontological concepts and their corresponding instance-view
entities. The intra-view models are trained to capture the structured knowledge
of instance and ontology views in separate embedding spaces, with a
hierarchy-aware encoding technique enabled for ontologies with hierarchies. We
explore multiple representation techniques for the two model components and
investigate with nine variants of JOIE. Our model is trained on large-scale
knowledge bases that consist of massive instances and their corresponding
ontological concepts connected via a (small) set of cross-view links.
Experimental results on public datasets show that the best variant of JOIE
significantly outperforms previous models on instance-view triple prediction
task as well as ontology population on ontologyview KG. In addition, our model
successfully extends the use of KG embeddings to entity typing with promising
performance.

该论文提出了一种新颖的双视图知识图嵌入模型，JOIE，旨在产生更好的知识嵌入并实现依赖于多视图知识的新应用，其通过跨视图和内视图建模来学习知识库的多个方面，其中跨视图模型用于建立本体概念和其对应的实例视图实体之间的关联。