  Entity alignment aims to identify equivalent entity pairs between different
knowledge graphs (KGs). Recently, the availability of temporal KGs (TKGs) that
contain time information created the need for reasoning over time in such TKGs.
Existing embedding-based entity alignment approaches disregard time information
that commonly exists in many large-scale KGs, leaving much room for
improvement. In this paper, we focus on the task of aligning entity pairs
between TKGs and propose a novel Time-aware Entity Alignment approach based on
Graph Neural Networks (TEA-GNN). We embed entities, relations and timestamps of
different KGs into a vector space and use GNNs to learn entity representations.
To incorporate both relation and time information into the GNN structure of our
model, we use a time-aware attention mechanism which assigns different weights
to different nodes with orthogonal transformation matrices computed from
embeddings of the relevant relations and timestamps in a neighborhood.
Experimental results on multiple real-world TKG datasets show that our method
significantly outperforms the state-of-the-art methods due to the inclusion of
time information.
