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

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 AKT1 and SWRC2

A framework for ontology-based applications (Vitro)• Jena3 model + ontology editor + simple CMS• Real-time inferencing for a production environment

vivo.cornell.edu

VIVO faculty profile

Sample search in VIVO: “proteom*”

Vitro layered system structure

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

Next steps

• Leveraging SPARQL4 or SWRL5 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

(OWLIM6 and/or Pellet7)

• Exploring OWL 1.18 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 Fresnel9

Defined classes

• Use inferencing from classes, properties, and their values to group and/or filter content

OWL 1.1 property chaining

• Data reported by country can be retrieved by region or continent

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 Fedora9 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

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/