“Know-Evolve: Deep Reasoning in Temporal Knowledge Graphs” Knowledge Graphs are important tools to model multi-relational data that serves as information pool for various applications. Traditionally, these graphs are considered to be static in nature. However, recent availability of large scale event-based interaction data has given rise to dynamically evolving knowledge graphs that contain temporal information for each edge. Reasoning over time in such graphs is not yet well understood. In this paper, we present a novel deep evolutionary knowledge network architecture to learn entity embeddings that can dynamically and non-linearly evolve over time. We further propose a multivariate point process framework to model the occurrence of a fact (edge) in continuous time. To facilitate temporal reasoning, the learned embeddings are used to compute relationship score that further parametrizes intensity function of the point process. We demonstrate improved performance over various existing relational learning models on two large scale real-world datasets. Further, our method effectively predicts occurrence or recurrence time of a fact which is novel compared to any prior reasoning approaches in multi-relational setting.