  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.
