Learning how to predict future events from patterns of past events is
difficult when the set of possible event types is large. Training an
unrestricted neural model might overfit to spurious patterns. To exploit
domain-specific knowledge of how past events might affect an event's present
probability, we propose using a temporal deductive database to track structured
facts over time. Rules serve to prove facts from other facts and from past
events. Each fact has a time-varying state---a vector computed by a neural net
whose topology is determined by the fact's provenance, including its experience
of past events. The possible event types at any time are given by special
facts, whose probabilities are neurally modeled alongside their states. In both
synthetic and real-world domains, we show that neural probabilistic models
derived from concise Datalog programs improve prediction by encoding
appropriate domain knowledge in their architecture.

利用时态推理数据库跟踪结构化事实来探索过去事件如何影响事件的现有概率，并通过神经概率模型来提高预测准确率。