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The VIVO Ontology Project
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  • Transcript

    • 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.
    • 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
      • AKT
      • SWRC http://
      • Jena
      • SPARQL
      • SWRL
      • OWLIM
      • Pellet
      • Fresnel
      • Fedora