  Entity alignment (EA) aims at building a unified Knowledge Graph (KG) of rich
content by linking the equivalent entities from various KGs. GNN-based EA
methods present promising performances by modeling the KG structure defined by
relation triples. However, attribute triples can also provide crucial alignment
signal but have not been well explored yet. In this paper, we propose to
utilize an attributed value encoder and partition the KG into subgraphs to
model the various types of attribute triples efficiently. Besides, the
performances of current EA methods are overestimated because of the name-bias
of existing EA datasets. To make an objective evaluation, we propose a hard
experimental setting where we select equivalent entity pairs with very
different names as the test set. Under both the regular and hard settings, our
method achieves significant improvements ($5.10\%$ on average Hits@$1$ in
DBP$15$k) over $12$ baselines in cross-lingual and monolingual datasets.
Ablation studies on different subgraphs and a case study about attribute types
further demonstrate the effectiveness of our method. Source code and data can
be found at https://github.com/thunlp/explore-and-evaluate.
