Thursday, June 24, 2010
02:00 PM - 03:30 PM
|Level: ||Technical - Intermediate|
Formal ontologies developed using RDF/OWL provide an ideal means for establishing a “vocabulary of discourse” for a given domain through the definition of relevant classes and relationships. It is common practice to use an ontology as the basis for a data model implemented in a language such as Java or C++, around which applications are then developed. While this approach can be effective, the real value in using an ontology comes from the ontology language’s formal semantics that enable the automatic deduction of information implicit in the data assertions made using the ontology.
An inference engine or reasoner is commonly used to invoke the semantics of the language defined in terms of explicit formal axioms. Some reasoners are solely dedicated to ontological reasoning while others are more generic and can be used for purposes in addition to ontological reasoning. In most real-world problems, ontology languages such as OWL are able to capture some but not all of the knowledge required for the domain. This talk will focus on the use of generic forward-chaining inference engines to solve real-world problems using OWL 2 RL axioms to perform ontological reasoning in conjunction with a knowledge base of rules to perform higher level reasoning over the ontologically represented data.
Topics to be covered include the following:
- What is OWL 2 RL and how is it implemented on forward chaining inference engines?
- What types of automatic inferencing can be achieved using OWL 2 RL?
- What types must be implemented as user defined rules?
- How can this approach be applied to practical problems?
- What open source and commercial forward-chaining inference engines are available?
- What are some of their strengths and weaknesses?
Christopher J. Matheus leads the definition of VIStology's technical direction and research programs with more than twenty five years of experience and expertise in the areas of Semantic Web technologies, artificial intelligence, interactive Internet applications, machine learning/knowledge discovery, and technology management. His prior experience includes R&D positions at Oak Ridge National Laboratories and GTE Laboratories (now Verizon Technologies) as well as management and product R&D roles with a number of Boston-area, technology-based start-up companies.
Dr. Matheus has more than forty-five technical publications, is a Leslie Warner Technical Achievement Awardee, and is a former Thomas J. Watson Fellow. Chris obtained his M.S. and Ph.D. degrees in Computer Science from the University of Illinois at Urbana-Champaign and holds a B.A. in Physics from Lawrence University of Wisconsin.