  The proliferation of Knowledge Graphs (KGs) that support a wide variety of
applications, like entity search, question answering and recommender systems,
has led to the need for identifying overlapping information among different
KGs. Entity Alignment (EA) is the problem of detecting such overlapping
information among KGs that refer to the same real-world entities. Recent works
have shown a great potential in exploiting KG embeddings for the task of EA,
with most works focusing on the structural representation of entities (i.e.,
entity neighborhoods) in a KG and some works also exploiting the available
factual information of entities (e.g., their names and associated literal
values). However, real-word KGs exhibit high levels of structural and semantic
heterogeneity, making EA a challenging task in which most existing methods
struggle to achieve good results. In this work, we propose HybEA, an
open-source EA method that focuses on both structure and facts, using two
separate attention-based models. Our experimental results show that HybEA
outperforms state-of-the-art methods by at least 5% and as much as 20+% (with
an average difference of 11+%) Hits@1, in 5 widely used benchmark datasets.
