CS 9010: Semantic Web

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