Knowledge graphs, breaking down silos, data science

TITLE: Banish your Silos with Knowledge Graphs ABSTRACT: Semantic networks have been around since the dawn of AI. They evolved and matured into the knowledge graphs that are in use by most large companies today. We focus on one important use case for knowledge graphs in the enterprise: getting rid of silos. We describe how conventional relational technology strongly encourages silos and how these problems are avoided when using knowledge graphs based on the W3C’s suite of Semantic Web standards. We illustrate with examples from industry and warn about the siren call of semantic silos. We close by describing the growing synergy between knowledge graphs and other current hot topics in AI: machine learning and natural language processing.
Who is this presentation for?
CIOs, CTOs, people who are fed up with complexity and silos.
Prerequisite knowledge:

What you'll learn?
How to break down silos using knowledge graphs and ontologies

Michael Uschold has thirty years’ experience in developing and transitioning semantic technology from academia to industry. He pioneered the field of ontology engineering, co-authoring the first paper and giving the first tutorial on the topic in 1995 in the UK. As a senior ontology consultant at Semantic Arts since October 2010, Michael trains and guides clients to better understand and leverage semantic technology using knowledge graphs. He has built commercial enterprise ontologies in finance, healthcare, legal research, consumer products, electrical devices, manufacturing, corporation registration, and digital asset management. The ontologies are used to create knowledge graphs that drive production applications. During 2008-2009, Uschold worked at Reinvent on a team that developed a semantic advertising platform that substantially increased revenue. As a research scientist at Boeing from 1997-2008 he defined, led and participated in numerous projects applying semantic technology to enterprise challenges. He is a frequent invited speaker and panelist at national and international events, and serves on the editorial board of the Applied Ontology Journal. He received his Ph.D. in AI from Edinburgh University in 1991 and an MSc. from Rutgers University in Computer Science in 1982.