  The flourishing of knowledge graph applications has driven the need for
entity alignment (EA) across KGs. However, the heterogeneity of practical KGs,
characterized by differing scales, structures, and limited overlapping
entities, greatly surpasses that of existing EA datasets. This discrepancy
highlights an oversimplified heterogeneity in current EA datasets, which
obstructs a full understanding of the advancements achieved by recent EA
methods. In this paper, we study the performance of EA methods in practical
settings, specifically focusing on the alignment of highly heterogeneous KGs
(HHKGs). Firstly, we address the oversimplified heterogeneity settings of
current datasets and propose two new HHKG datasets that closely mimic practical
EA scenarios. Then, based on these datasets, we conduct extensive experiments
to evaluate previous representative EA methods. Our findings reveal that, in
aligning HHKGs, valuable structure information can hardly be exploited through
message-passing and aggregation mechanisms. This phenomenon leads to inferior
performance of existing EA methods, especially those based on GNNs. These
findings shed light on the potential problems associated with the conventional
application of GNN-based methods as a panacea for all EA datasets.
Consequently, in light of these observations and to elucidate what EA
methodology is genuinely beneficial in practical scenarios, we undertake an
in-depth analysis by implementing a simple but effective approach: Simple-HHEA.
This method adaptly integrates entity name, structure, and temporal information
to navigate the challenges posed by HHKGs. Our experiment results conclude that
the key to the future EA model design in practice lies in their adaptability
and efficiency to varying information quality conditions, as well as their
capability to capture patterns across HHKGs.
