Title: Know-Evolve: Deep Temporal Reasoning for Dynamic Knowledge Graphs,
Abstract: The availability of large scale event data with time stamps has given rise to
dynamically evolving knowledge graphs that contain temporal information for
each edge. Reasoning over time in such dynamic knowledge graphs is not yet well
understood. To this end, we present Know-Evolve, a novel deep evolutionary
knowledge network that learns non-linearly evolving entity representations over
time. The occurrence of a fact (edge) is modeled as a multivariate point
process whose intensity function is modulated by the score for that fact
computed based on the learned entity embeddings. We demonstrate significantly
improved performance over various relational learning approaches on two large
scale real-world datasets. Further, our method effectively predicts occurrence
or recurrence time of a fact which is novel compared to prior reasoning
approaches in multi-relational setting.