The VIVO Ontology Project
Upcoming SlideShare
Loading in...5

Like this? Share it with your network





Total Views
Views on SlideShare
Embed Views



0 Embeds 0

No embeds



Upload Details

Uploaded via as Microsoft PowerPoint

Usage Rights

CC Attribution-NonCommercial-ShareAlike LicenseCC Attribution-NonCommercial-ShareAlike LicenseCC Attribution-NonCommercial-ShareAlike License

Report content

Flagged as inappropriate Flag as inappropriate
Flag as inappropriate

Select your reason for flagging this presentation as inappropriate.

  • Full Name Full Name Comment goes here.
    Are you sure you want to
    Your message goes here
Post Comment
Edit your comment
  • Duplicate slide to maintain title and subtitle formatting
  • Duplicate slide to maintain title and subtitle formatting
  • Duplicate slide to maintain title and subtitle formatting
  • Duplicate slide to maintain title and subtitle formatting

The VIVO Ontology Project Presentation 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