  Knowledge graphs (KGs) typically contain temporal facts indicating
relationships among entities at different times. Due to their incompleteness,
several approaches have been proposed to infer new facts for a KG based on the
existing ones-a problem known as KG completion. KG embedding approaches have
proved effective for KG completion, however, they have been developed mostly
for static KGs. Developing temporal KG embedding models is an increasingly
important problem. In this paper, we build novel models for temporal KG
completion through equipping static models with a diachronic entity embedding
function which provides the characteristics of entities at any point in time.
This is in contrast to the existing temporal KG embedding approaches where only
static entity features are provided. The proposed embedding function is
model-agnostic and can be potentially combined with any static model. We prove
that combining it with SimplE, a recent model for static KG embedding, results
in a fully expressive model for temporal KG completion. Our experiments
indicate the superiority of our proposal compared to existing baselines.
