Knowledge graph embeddings (KGE) have been extensively studied to embed
large-scale relational data for many real-world applications. Existing methods
have long ignored the fact many KGs contain two fundamentally different views:
high-level ontology-view concepts and fine-grained instance-view entities. They
usually embed all nodes as vectors in one latent space. However, a single
geometric representation fails to capture the structural differences between
two views and lacks probabilistic semantics towards concepts' granularity. We
propose Concept2Box, a novel approach that jointly embeds the two views of a KG
using dual geometric representations. We model concepts with box embeddings,
which learn the hierarchy structure and complex relations such as overlap and
disjoint among them. Box volumes can be interpreted as concepts' granularity.
Different from concepts, we model entities as vectors. To bridge the gap
between concept box embeddings and entity vector embeddings, we propose a novel
vector-to-box distance metric and learn both embeddings jointly. Experiments on
both the public DBpedia KG and a newly-created industrial KG showed the
effectiveness of Concept2Box.

Concept2Box 提出了一种新方法，它使用双重几何表示技术联合嵌入了一个 KG 的两个视图：本体视图概念和实例视图实体。这种方法使用盒形嵌入来模拟概念，并使用向量来模拟实体，并提出了一种新的向量到盒形距离度量方法。