Wednesday, June 6, 2012 
04:30 PM - 05:15 PM
| Level: | Technical - Intermediate
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| Location: | Franciscan A |
We give practical tips and discuss a wide range of issues and experiences after building a real world enterprise ontology of over 1200 classes and 400 properties for our client. Practical considerations include choosing and using languages and tools and how/whether to leverage existing upper- and middle-level ontologies. We also look at a variety of situations commonly arising in business that are challenging to represent. These include: events and how to influence and regulate behavior; goals and intentions; skills and capabilities, registration and credentialing.
Audience: anyone who wants some technical depth and practical tips in how to build a real-world enterprise ontology.
Key Challenges and Accomplishments: • representing complex ideas in a way that business people can understand them • exploiting OWL2, as well as working around its limitations • inference and debugging with substantial scale ontologies
Conclusions: • We saved a lot of time using gist, a business-oriented enterprise upper ontology • It will be even faster next time
Michael Uschold is an internationally recognized expert with over two decades 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. This leveraged the work he did in creating the influential "Enterprise Ontology". From November 2010, he has been building commercial enterprise ontologies for Semantic Arts. Before that, he worked 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 regularly serves on advisory boards. He has given numerous tutorials and training classes.
He received his Ph.D. in AI from Edinburgh University in 1991.
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