Chen Yong Cher | Enterprise Knowledge Graph and Machine Learning Integration
Knowledge Graph Conference 2020
•
19m
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.
Up Next in Knowledge Graph Conference 2020
-
Designing and Building Knowledge Grap...
Knowledge Graphs are fulfilling the vision of creating intelligent systems that integrate knowledge and data at large scale. We observe the adoption of Knowledge Graphs by the Googles of the world. However, not everybody is a Google. Enterprises still struggle to understand their relational datab...
-
Tutorial Introduction to Logic Knowle...
A hands-on tutorial that will introduce logic knowledge graphs via TerminusDB to those beginning or looking to develop their knowledge graph journey.
-
Panel | Frontiers in Data Intelligence
Panel: Frontiers in Data Intelligence. What is data intelligence? Who owns knowledge graphs? How do we define contexts of use for knowledge graphs? How to make sure knowledge graphs offer equitable utility?
