Entity alignment is to find identical entities in different knowledge graphs
(KGs) that refer to the same real-world object. Embedding-based entity
alignment techniques have been drawing a lot of attention recently because they
can help solve the issue of symbolic heterogeneity in different KGs. However,
in this paper, we show that the progress made in the past was due to biased and
unchallenging evaluation. We highlight two major flaws in existing datasets
that favor embedding-based entity alignment techniques, i.e., the isomorphic
graph structures in relation triples and the weak heterogeneity in attribute
triples. Towards a critical evaluation of embedding-based entity alignment
methods, we construct a new dataset with heterogeneous relations and attributes
based on event-centric KGs. We conduct extensive experiments to evaluate
existing popular methods, and find that they fail to achieve promising
performance. As a new approach to this difficult problem, we propose a
time-aware literal encoder for entity alignment. The dataset and source code
are publicly available to foster future research. Our work calls for more
effective and practical embedding-based solutions to entity alignment.

该论文介绍了发现不同知识图谱中相同实体的通用技术，使用嵌入式技术具有符号异构性的问题，在构建数据集时发现了两个主要不足之处：关系三元组中的同构图结构和属性三元组中的弱异质性，因此提出了一种基于时间感知的文字编码算法进行实体对齐。