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Enterprise Knowledge Graph and Machine Learning Integration with Live Data

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.
May 6th 2020, 12:20pm
cyber security, reasoning, knowledge graph, Regulatory compliance, machine learning, anti-money laundering

Chen-Yong Cher


Graphen, Inc.

Dr. Chen Yong Cher is the CTO of Infrastructure at Graphen. He oversees Graphen's core AI platform, Ardi, and some of its cybersecurity and risk management solutions catering to financial firms. Prior to Graphen, he was a System Researcher at IBM for 12 years. He was a contributor to BlueGene/Q supercomputer and DARPA's PERCS and PERFECT projects, and coauthored over 25 patents and 40 technical papers.

Lightening talks from the Main Stage along with speaker Q&A sessions

May 4-7, 2020


Knowledge Graphs form an organized and curated set of facts that provide support for models to help understand the world. This conference gathers technology leaders, researchers, academics, vendors — and most importantly, practitioners, who know the discipline. For KGC 2020, attendees can participate from wherever they want in the world, from the comfort of their homes. We will stream the content, provide access to our speakers and support chat and networking as well as give access to all of the content live and on-demand after the event.