  Knowledge graph (KG) embedding encodes the entities and relations from a KG
into low-dimensional vector spaces to support various applications such as KG
completion, question answering, and recommender systems. In real world,
knowledge graphs (KGs) are dynamic and evolve over time with addition or
deletion of triples. However, most existing models focus on embedding static
KGs while neglecting dynamics. To adapt to the changes in a KG, these models
need to be retrained on the whole KG with a high time cost. In this paper, to
tackle the aforementioned problem, we propose a new context-aware Dynamic
Knowledge Graph Embedding (DKGE) method which supports the embedding learning
in an online fashion. DKGE introduces two different representations (i.e.,
knowledge embedding and contextual element embedding) for each entity and each
relation, in the joint modeling of entities and relations as well as their
contexts, by employing two attentive graph convolutional networks, a gate
strategy, and translation operations. This effectively helps limit the impacts
of a KG update in certain regions, not in the entire graph, so that DKGE can
rapidly acquire the updated KG embedding by a proposed online learning
algorithm. Furthermore, DKGE can also learn KG embedding from scratch.
Experiments on the tasks of link prediction and question answering in a dynamic
environment demonstrate the effectiveness and efficiency of DKGE.
