SCOUT: Smart Communication Of Unexpected Threats

The Navy needs to fuse and distill time-stamped data sources as varied as overhead imagery and Twitter feeds into actionable intelligence such as alerts, on-demand reports about entities of interest, and search capabilities. In order to enable such analytics, it is effective to learn fixed dimensional vectors (embeddings) representing the entities present in these heterogeneous data sources, which many machine learning tools require as input. While the literature on embedding knowledge graphs abounds, little work has been done to learn time-dependent embeddings for entities present in heterogeneous timestamped knowledge graphs, designed to drive predictions of what an entity is likely to do next, or for anomaly detection and alerting. We propose to address this gap by using deep sequence embedding techniques borrowed from the Computer Vision and NLP communities.In order to make our fused embedding product intelligible and tangible, we propose a data-driven report generator capable of displaying relevant known and inferred attributes for all the types of entities present in the various data sources, with minimal setup costs or requirements. We also propose a method for identifying and ranking which raw data statements most contributed to a shift in an entitys embedding, making embedding changes concrete and understandable.