Thursday, November 16, 2017
08:30 AM - 04:00 PM
|Level: ||Technical - Intermediate|
Part 1: Introducing Ontology and OWL – This is a lively interactive session where we learn together by doing. Building and populating business ontologies in triple stores is of growing importance to the enterprise, helping to reduce complexity and improve flexibility in enterprise systems. We introduce the following foundational building blocks for an ontology, starting with informal terminology and transitioning to the technical terms of OWL.
- Individual things are OWL individuals - e.g., JaneDoe
- Kinds of things are OWL classes - e.g., Organization
- Kinds of relationships are OWL properties - e.g., worksFor
Participants identify what the key things are in everyday subjects such as healthcare and finance and how they are related to each other. We start to build an ontology in healthcare, using this as a driver to introduce needed constructs in OWL. We demonstrate how to formally describe the meaning of concepts. We introduce some common patterns and pitfalls. We show how inference can assist the ontology engineering process. Key topics and learning points will be:
- An ontology is a model of some subject matter that you care about. It is represented as triples.
- The ontology is a semantic schema that gives the data meaning.
- Inference generates new triples and helps to ensure the correctness of the ontology.
- We present some of the more widely used patterns and most common pitfalls.
Part 2: Enterprise Ontology: a hot knife through complexity - An enterprise ontology is a small and elegant representation of the core concepts in your enterprise that are stable over time. We introduce gist, an upper enterprise ontology containing a set of generic enterprise concepts used to kick-start enterprise ontology development. Its scope includes people, organizations, agreements, physical things, places, content, time, and events. We explain how enterprise ontology is used to create semantic solutions that are much simpler than conventional solutions. Removing so much complexity is the key to agility and is a natural fit for a data-centric architecture.
- URIs and triples make it possible to share schema and dramatically simplifies data and application integration.
- gist: an Upper Enterprise Ontology so you don’t have to reinvent the wheel
- Using an enterprise ontology underpins a data-centric architecture and is the key to avoiding the creation of more silos.
- Building a semantic application using SPARQL, SHACL, and R2RML
- Case studies
- How does governance work for ontologies and semantic solutions?
Michael Uschold is an internationally recognized pioneer in engineering and applying ontologies. He has given numerous invited talks and regularly serves on advisory boards. In 2008-2009, Michael was senior ontologist at Reinvent Inc. His team created a commercial grade contextual advertising system. From 1997-2008, Michael worked as a research scientist at The Boeing Company working on a variety of projects, e.g. in the area of semantic interoperability and semantic filtering. From 1983-1997, he held positions at the University of Edinburgh. He was a senior member of technical staff in the Artificial Intelligence Applications Institute (AIAI) and was also a lecturer and a research associate at the Department of Artificial Intelligence. He received his B.S. in mathematics and physics at Canisius College in Buffalo, N.Y. in 1977, a Master's in computer science from Rutgers University in 1982, and a Ph.D. in Artificial Intelligence from the University of Edinburgh in 1991.
Mark Ouska is an Enterprise Agile Data Strategist and Ontologist with nearly 30 years of professional information management experience in Enterprise Business Data Architecture. He's an Enterprise Data Strategy expert focused on data leadership and enterprise information management strategy development. He's demonstrated success recruiting key cross-functional business segments to participate in the accurate technical articulation and execution of business data goals and objectives. He has proven expertise in extracting business data requirements, developing relationships, and evolving models that execute the enterprise data vision. He has public and private-sector leadership experience implementing solutions that are willingly adopted by technical staff, critical business leadership, and championed by VP and C-Level constituents. He has experience in multiple industries, including Pharmaceutical Research, Retail, Health Care, Financial Services, Consulting, Criminal Justice, Insurance, Natural Resources, Petrochemicals, and Software Development.