1. The VIVO Ontology Project Technology: Jon Corson-Rikert, Brian Caruso, Brian Lowe, Nick Cappadona Project Coordination: Medha Devare, Elaine Guidero, Jaron Turner Content Editors: Medha Devare, Nan Hyland, Jim Morris-Knower, Jill Powell, Deb Schmidle, Gail Steinhart, Kornelia Tancheva, Susanne Whitaker September 21, 2007

2. What is VIVO? A Web resource Single point of access for information on scholarly activity at Cornell Independent of Cornell’s administrative structure Search or browse directly or syndicate via web services An ontology-based application Represents common university relationships Patterned on AKT 1 and SWRC 2 A framework for ontology-based applications (Vitro) Jena 3 model + ontology editor + simple CMS Real-time inferencing for a production environment

3. vivo.cornell.edu

4. VIVO faculty profile

5. Sample search in VIVO: “proteom*”

6. Vitro layered system structure

7. Current development Direct import and export of OWL RDF –Maintain class and property provenance from source ontologies –Import or supplement content –Extend the unified model to bridge across domains –Deliver integrated view from multiple distinct data models Using in-memory Jena model for speed while concurrently maintaining a Jena database persistence layer Granular authorization controlling direct end-user editing –Cornell single sign-on; others create accounts –Edit own information except that from University databases of record –Allow for proxies Jena reified statements (in a separate model) to track who did what when

8. Next steps Leveraging SPARQL 4 or SWRL 5 for complex, relationship-based queries –Currently pull content by class and hard-wired filters –Create browse groups or web service filters via SPARQL query Assigning membership in defined classes by inference –Avoid time-consuming and error-prone manual tagging –Changes reflected in real time using plug-in inference engines (OWLIM 6 and/or Pellet 7 ) Exploring OWL 1.1 8 and other extensions –“Transitive over” object properties (property chain inclusion axioms) –“Defined properties” (functional data properties, data property assertion) More flexible filtering and ordering for display, possibly using Fresnel 9

9. Defined classes Use inferencing from classes, properties, and their values to group and/or filter content

10. OWL 1.1 property chaining Data reported by country can be retrieved by region or continent

11. Future opportunities Selective and dynamic content integration –Import and manage multiple ontologies or portions thereof, retaining original class and property relationships –Pull in only enough metadata from remote data sources to allow discovery and the desired level of integration, without replicating all content –Build higher-level defined classes and properties to bridge across ontologies within Vitro –Link out to or query remote sources for updated content, statistics, and/or data eScience, eHumanities –Use Vitro as a front end to Fedora 9 and other repository platforms –Create and manage terminology and cross-disciplinary relationships for distributed collections while retaining original metadata schemas –Facilitate blending distributed data resources into scholarly publishing and other academic exchanges

12. References 1. AKT http://kmi.open.ac.uk/projects/akt/ref-onto/ 2. SWRC http:// www.aifb.uni-karlsruhe.de/WBS/ysu/publications/2005_swrc_baosw.pdf 3. Jena http://jena.sourceforge.net 4. SPARQL http://www.w3.org/TR/rdf-sparql-query/ 5. SWRL http://www.w3.org/Submission/SWRL/ 6. OWLIM http://www.ontotext.com/owlim/ 7. Pellet http://pellet.owldl.com/ 8. Fresnel http://simile.mit.edu/wiki/Fresnel 9. Fedora http://www.fedora-commons.org/