Irregular data in real-world are usually organized as heterogeneous graphs
(HGs) consisting of multiple types of nodes and edges. To explore useful
knowledge from real-world data, both the large-scale encyclopedic HG datasets
and corresponding effective learning methods are crucial, but haven't been well
investigated. In this paper, we construct a large-scale HG benchmark dataset
named UniKG from Wikidata to facilitate knowledge mining and heterogeneous
graph representation learning. Overall, UniKG contains more than 77 million
multi-attribute entities and 2000 diverse association types, which
significantly surpasses the scale of existing HG datasets. To perform effective
learning on the large-scale UniKG, two key measures are taken, including (i)
the semantic alignment strategy for multi-attribute entities, which projects
the feature description of multi-attribute nodes into a common embedding space
to facilitate node aggregation in a large receptive field; (ii) proposing a
novel plug-and-play anisotropy propagation module (APM) to learn effective
multi-hop anisotropy propagation kernels, which extends methods of large-scale
homogeneous graphs to heterogeneous graphs. These two strategies enable
efficient information propagation among a tremendous number of multi-attribute
entities and meantimes adaptively mine multi-attribute association through the
multi-hop aggregation in large-scale HGs. We set up a node classification task
on our UniKG dataset, and evaluate multiple baseline methods which are
constructed by embedding our APM into large-scale homogenous graph learning
methods. Our UniKG dataset and the baseline codes have been released at
this https URL

通过构建大规模异构图数据集 UniKG 并采用语义对齐策略和异质图传播模块 (APM) 实现多属性实体的高效信息传播和多跳聚合，该方法在 UniKG 数据集上进行基于节点分类的评估，扩展了大规模均匀图研究方法在异构图上的应用。

UniKG: 大规模知识图谱的基准和通用嵌入

UniKG: A Benchmark and Universal Embedding for Large-Scale Knowledge  Graphs

Most researches for knowledge graph completion learn representations of
entities and relations to predict missing links in incomplete knowledge graphs.
However, these methods fail to take full advantage of both the contextual
information of entity and relation. Here, we extract contexts of entities and
relations from the triplets which they compose. We propose a model named AggrE,
which conducts efficient aggregations respectively on entity context and
relation context in multi-hops, and learns context-enhanced entity and relation
embeddings for knowledge graph completion. The experiment results show that
AggrE is competitive to existing models.

提出了一种名为 AggrE 的模型，该模型通过对实体和关系的上下文进行多跳的高效聚合，学习增强上下文的实体和关系嵌入，以完成知识图的补全。实验结果表明，AggrE 模型竞争力强。