  Embedding learning, a.k.a. representation learning, has been shown to be able
to model large-scale semantic knowledge graphs. A key concept is a mapping of
the knowledge graph to a tensor representation whose entries are predicted by
models using latent representations of generalized entities. Knowledge graphs
are typically treated as static: A knowledge graph grows more links when more
facts become available but the ground truth values associated with links is
considered time invariant. In this paper we address the issue of knowledge
graphs where triple states depend on time. We assume that changes in the
knowledge graph always arrive in form of events, in the sense that the events
are the gateway to the knowledge graph. We train an event prediction model
which uses both knowledge graph background information and information on
recent events. By predicting future events, we also predict likely changes in
the knowledge graph and thus obtain a model for the evolution of the knowledge
graph as well. Our experiments demonstrate that our approach performs well in a
clinical application, a recommendation engine and a sensor network application.
