Exploration of a Data Landscape using a Collaborative Linked Data Framework.


Published on

In Proceedings of the The Future of the Web for Collaborative Science, WWW2010 (Raleigh, North Carolina, April 26, 2010)

Published in: Technology, Education
  • Be the first to comment

  • Be the first to like this

Exploration of a Data Landscape using a Collaborative Linked Data Framework.

  1. 1. Exploration of a Data Landscape using a Collaborative Linked Data Framework Laurent Alquier, Project Lead, Informatics CoE
  2. 2. Scientists are looking for answers to translational questions Background images from Flickr - CC Share Alike 2.0 Basic Research Prototype Design or Discovery Preclinical Development Clinical Development FDA Filing, Approval & Launch Prep Translational Research Critical Path Research
  3. 3. Instead they research how to find and access data. Which data source should I choose ? Can I access it ? Who should I talk to ? Scientists want to do Science, not Data Management. Which interfaces are available ?
  4. 4. knowIT helps scientists and IT manage what they know about Information Systems.
  5. 5. Collaborative cataloging of data sources
  6. 6. Structured data inside wiki pages
  7. 7. Advantages of Semantic MediaWiki (SMW) Wiki pages are subjects for Semantic annotations (triples : page – property – value) MediaWiki knowledge management tools Many ways to input and output content.
  8. 8. <ul><li>Provide a flexible integrative data layer </li></ul><ul><ul><ul><li>Linked Data concepts can be used for flexible data integration of internal and external sources of disparate data </li></ul></ul></ul><ul><li>Enable scientists to answer novel translational questions </li></ul><ul><ul><li>Linked Data queries support diverse translational scientific questions </li></ul></ul>Linked Data Pilot for Neuroscience
  9. 9. Overview of the Linked Data Framework RDF CSV XML RDBMS Docs RDB2RDF mapping TM J&J Ontol. Data catalog Data browser & Visualization Ontology Curation J&J RDF SPARQL Semantic Wiki
  10. 10. Reviewing W3C’s Translational Medicine Ontology (TMO) as a base for J&J Ontology
  11. 11. <ul><ul><li>Data Source discovery </li></ul></ul><ul><ul><ul><li>Provides framework with provenance information </li></ul></ul></ul><ul><ul><li>Resource discovery </li></ul></ul><ul><ul><ul><li>Extracts relationships between concepts across data sources </li></ul></ul></ul><ul><ul><li>SPARQL Querying </li></ul></ul><ul><ul><ul><li>Constructs and captures queries, parses results </li></ul></ul></ul><ul><ul><li>Inferencing </li></ul></ul><ul><ul><ul><li>Pulls pre-constructed rule sets hosted in server directory </li></ul></ul></ul>C# API allows programmatic abstraction of….
  12. 12. Ask questions using federated queries
  13. 13. Integration with analytical tools
  14. 14. Improved enterprise search
  15. 15. Analysis of data sources content Relfinder
  16. 16. Visualization of relationships between data sources
  17. 17. Next steps Automation and integration with existing ontologies Enable self-describing data sources Dynamic, layered map of data landscape Incorporate additional data sources Drive broader awareness and adoption of the tool
  18. 18. <ul><li>We would like to thank current and past contributors for their patience, ideas and support : </li></ul><ul><ul><li>Tim Schultz </li></ul></ul><ul><ul><li>Susie Stephens </li></ul></ul><ul><ul><li>Dimitris Agrafiotis </li></ul></ul><ul><ul><li>Rudi Verbeek </li></ul></ul><ul><ul><li>John Stong </li></ul></ul><ul><ul><li>Joe Ciervo </li></ul></ul><ul><ul><li>Keith McCormick </li></ul></ul>Acknowledgements
  19. 19. <ul><li>Laurent Alquier </li></ul><ul><li>Software engineer, Project lead </li></ul><ul><li>Informatics CoE </li></ul><ul><li>[email_address] </li></ul>