CTPs: Contextual Temporal Profiles for Time Scoping Facts using State Change Detection

Temporal scope adds a time dimension to facts in Knowledge Bases (KBs). These time scopes specify the time periods when a given fact was valid in real life. With-out temporal scope, many facts are under-specified, reducing the usefulness of the data for upper level applications such as Question Answering. Existing methods for temporal scope inference and extrac-tion still suffer from low accuracy. In this paper, we present a new method that lever-ages temporal profiles augmented with context — Contextual Temporal Profiles (CTPs) of entities. Through change pat-terns in an entity’s CTP, we model the en-tity’s state change brought about by real world events that happen to the entity (e.g, hired, fired, divorced, etc.). This leads to a new formulation of the temporal scoping problem as a state change detection prob-lem. Our experiments show that this for-mulation of the problem, and the resulting solution are highly effective for inferring temporal scope of facts.