In the realm of enterprise applications such as cybersecurity and anti-money laundering (AML), data and system engineers team up to deal with interconnected data of great scale and richness. The regulatory need adds requirements to instant tracibility and explanability of data and analytic models, to aid and reduce human workloads. Moreover, the teams have to deal with reliability and timeliness of data and events that are of dubious precision and cleanliness. In this work, we will share our real-world experiences of integrating streaming graph operations, with automatically tuned analytics including Graph Behavior Learning, Machine Learning and Bayesian Reasoning. We will also describe the use of imperfect learning which is critical to real-time enterprise applications.
Knowledge graphs are increasingly built using complex multifaceted machine learning based systems relying on a wide of different data sources. To be effective these must constantly evolve and thus be maintained. I present work on combining knowledge graph construction (e.g. information extraction...
