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CS 9010: Semantic Web

CS 9010: Semantic Web. Applications and Ontology Engineering Paula Matuszek Spring, 2006. Applications. So what are we going to do with all this? Review of several existing projects Setting Problem Contribution of web technology Discussion of other projects or potential projects.

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CS 9010: Semantic Web

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  1. CS 9010: Semantic Web Applications and Ontology Engineering Paula Matuszek Spring, 2006 CSC 9010 Spring, 2006. Paula Matuszek. Paula.A.Matuszek@gsk.com

  2. Applications • So what are we going to do with all this? • Review of several existing projects • Setting • Problem • Contribution of web technology • Discussion of other projects or potential projects CSC 9010 Spring, 2006. Paula Matuszek. Paula.A.Matuszek@gsk.com

  3. Elsevier • Setting: scientific publisher, traditional “journal” organization of content, traditional costing and access model. • Problem: organization is vertical, interests are horizontal • Semantic Web contribution: • Ontologies and thesauri for richer search • RDF as a generic representation for integration • Comments? CSC 9010 Spring, 2006. Paula Matuszek. Paula.A.Matuszek@gsk.com

  4. Audi • Large auto manufacturer, 50K employees, 700K cars/year, many databases • Problem: sharing information across many sources with differing semantics • Semantic Web contribution: • Ontology as semantic data model • Individual sources mapped to ontology • Applications go through data tool • Comments? CSC 9010 Spring, 2006. Paula Matuszek. Paula.A.Matuszek@gsk.com

  5. Swiss Life • Setting: Large Life Insurer, 11K employees, geographically diverse. Tacit knowledge a major resource. • Problem: How to find who knows something or has some skill? • Semantic Web Contribution: • Ontology captures skills and organization • Ontology used to drive form for employees • Comments? CSC 9010 Spring, 2006. Paula Matuszek. Paula.A.Matuszek@gsk.com

  6. Web Services • Setting: web sites that provide a service to users – shopping, travel, banking • Problem: web sites heavily hand-developed, very tailored to specific application, aimed only at human interaction. • Semantic Web Contribution: • Assemble complex services from profiles of simpler services • Define interactions formally so communication can be between agents instead of requiring human intervention • Comments? CSC 9010 Spring, 2006. Paula Matuszek. Paula.A.Matuszek@gsk.com

  7. Ontology Engineering • Going from a domain that we want to represent to an actual ontology isn’t trivial! • The process of building and maintaining an ontology is called ontology engineering. • We have already discussed some of this as we talked about ontologies. CSC 9010 Spring, 2006. Paula Matuszek. Paula.A.Matuszek@gsk.com

  8. determinescope considerreuse enumerate terms defineclasses defineproperties defineconstraints createinstances Ontology-Development Process General approach: Usually a highly iterative process. CSC 9010 Spring, 2006. Paula Matuszek. Paula.A.Matuszek@gsk.com

  9. Problems with Hand-Crafted Ontologies CSC 9010 Spring, 2006. Paula Matuszek. Paula.A.Matuszek@gsk.com

  10. Problems with Hand-Crafting • Requires both skill in building ontologies and knowledge of the domain • Tremendously time consuming, even with tools like Protégé. • Easy to end up with errors • Inheritance • Defaults • Instances • Hard to keep up to date. CSC 9010 Spring, 2006. Paula Matuszek. Paula.A.Matuszek@gsk.com

  11. What Can Help? Some Technologies • NLP tools • Term extraction • Document summarization and topic tree creation (www.megaputer.com) • Entity and relationship extraction to get instances • Machine Learning • Association-rule-learning algorithms • Bayesian Classifiers • Rule induction • Clustering Note: these are all classification tools of some kind CSC 9010 Spring, 2006. Paula Matuszek. Paula.A.Matuszek@gsk.com

  12. What Can Help? Semi-automated Ontology Building • Extract from existing data on the web with Google-type search • Pasca • Matuszek et al • KCVC: knowledge contribution from volunteer contributors • FACTory • ESPGame CSC 9010 Spring, 2006. Paula Matuszek. Paula.A.Matuszek@gsk.com

  13. Knowledge Management • Ontologies represented in OWL/RDF/XML have many of the same issues as any software development project • Change Management and tracking • Permissions and ownership • Components and reuse CSC 9010 Spring, 2006. Paula Matuszek. Paula.A.Matuszek@gsk.com

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