Linked Open Data_mlanet13

  • View

  • Download

Embed Size (px)


Presentation at 2013 Medical Library Association Annual Meeting Layne Johnson and Kristi Holmes

Text of Linked Open Data_mlanet13

  • 1.An Introduction to the Semantic Web and Linked Open Data Kristi L. Holmes, PhD Twitter: @kristiholmes Layne Mark Johnson, PhD @LayneJohnson The day after May the Fourth, 2013

2. Information Overload 3. We humans have always applied tools to our work to make things work easier 4. Simple Machines 5. a web of data that can be processed directly and indirectly by machines -Tim Berners-Lee 6. At its heart, the Semantic Web is really about extending standard Web technologies to better deal with data on the Web. If the WWW is for people, the Semantic Web is for machines George Thomas and Jim Hendler, Data modeled as bidirectional relationships Semantic Web Value Proposition Web-based infrastructure of standards and technologies which allows for a distributable, machine readable description of data that allows for stronger data and smart web application linkages 7. How the Semantic Web works Anakin Skywalker is Luke Skywalker's father. 8. How the Semantic Web works XML and RDF are at the heart of the Semantic Web. They give computers a structure in which to look for information and define relationships between resources. 9. An ontology is simply a vocabulary that describes objects and how they relate to one another. A schema is a method for organizing information 10. Using languages designed for data RDF | OWL | XML 11. Semantic web: describes methods and technologies to allow machines to understand the meaning or "semantics of information on the web. -- W3C director Sir Tim Berners-Lee Ontology: a formal representation of the knowledge by a set of concepts within a domain and the relationships between those concepts. -- Wikipedia 12. Lets talk about the data The Semantic Web isn't just about putting data on the web. It is about making links, so that a person or machine can explore the web of data. With linked data, when you have some of it, you can find other related data. 13. The 5 Stars of Linked Open Data AVAILABILITY & VALUE 14. Is your data 5-star?? The 5 Stars of Linked Open Data 15. The growth of Linked Data 20082007 2011 16. What kind of things are available as linked data? The LOD Cloud 17. Models and standards that allow for greater data exchange (and flexibility!) It takes layers and layers of metadata, logic and security to make the Web machine- readable. 18. Building a web of data Data Creators, Data Aggregators, & Data Consumers Repositories. Tools. Applications. Workflows 19. Ok! Now lets dig into a few good examples of how we can put these things to work 20. Linked Open Data and Biomedical Research: A Survey of Current International Efforts Kristi L. Holmes, PhD Twitter: @kristiholmes Layne Mark Johnson, PhD @LayneJohnson May 5, 2013 21. Theevolvingecosystemof information Courtesy Mike Conlon, U Florida 22. Projects. Research Networking Ontology 23. Research Networking Information about scholars is optimized using a Web-based infrastructure of standards and technologies which allows for a distributable, machine readable description of data that allows for stronger data and smart web application linkages across many universities, agencies, societies both within the US and abroad. Why is this important? Linked data infrastructure allows for Visualizations, research and clinical data integration, and deep semantic searching across multiple types and sources of data By breaking data out of traditional database silos, research networking platforms promote a network effect within a single site and across multiple sites The value of the network increases with the amount of linked data and applications that are available to consume the linked data. 24. The Semantic Web & Researcher Networking Increasing recognition of the value of semantic web standards Increasing momentum in support of semantic web technologies to facilitate research discovery Recommendations for researcher networking recently endorsed by the CTSA Consortium Steering Committee represent a new standard in researcher networking. Examples of applications that consume these rich data include: visualizations, enhanced multi-site search. Other utilities are in development across a wide range of topic areas. 25. Recommendations and Best Practices for Research Networking The Research Networking Recommendations were approved by the CTSA Consortium Executive and Steering Committee on October 25, 2011. Recommendations for Research Networking: Recommendation: All CTSAs should encourage their institution(s) to implement research networking tool(s) institution-wide that utilize RDF triples and an ontology compatible with the VIVO ontology. Recommendation: Information in people profiles at institutions should be publicly available as data as a general principle, specifically as Linked Open Data. To ensure quality of information, authoritative electronic data sources versus manual entry should be emphasized. Institutions will vary in the amount of information that they will include and make publicly available but the value is enhanced by the quality and quantity of information. Recommendation: Monitoring of the research networking landscape, technology, and tools should continue to be overseen by experts from the CTSA consortium (e.g., the Research Networking group of the Informatics KFC). 26. Research Networking Systems VIVO, Profiles, SciVal Experts, Stanfords CAP, Iowas Loki Encourage your RN provider to meet the recommendations for Researcher networking Better visibility Enhanced utility 27. Profiles text 28. VIVO This work is funded by the National Institutes of Health, U24 VIVO enjoys a robust open source, open community space to support implementation, adoption, and development efforts around the world. See 29. CTSAconnect Reveal Connections. Realize Potential. CTSAConnect Project Goals: Identify potential collaborators, relevant resources, and expertise across scientific disciplines Assemble translational teams of scientists to address specific research questions Approach: Create a semantic representation of clinician and basic science researcher expertise to enable Broad and computable representation of translational expertise Publication of expertise as Linked Data (LD) for use in other applications 30. 1/25/2015 CTSAconnect Reveal Connections. Realize Potential. Merging VIVO and eagle-i eagle-i is an ontology-driven application for collecting and searching research resources. VIVO is an ontology-driven application for collecting and displaying information about people. Both publish Linked Data. Neither addresses clinical expertise. CTSAconnect will produce a single Integrated Semantic Framework, a modular collection of ontologies that also includes clinical expertise eagle-i Resources VIV O People Coordination eagle-i VIV O Semantic Clinical activities 31. OpenPHACTS Open PHACTS Project To reduce the barriers to drug discovery in industry, academia and for small businesses, the Open PHACTS consortium is building the Open PHACTS Discovery Platform. This will be freely available, integrating pharmacological data from a variety of information resources and providing tools and services to question this integrated data to support pharmacological research. Guiding principle is open access, open usage, open source - Key to standards adoption - 32. OpenPHACTS Open PHACTS Project Develop a set of robust standards Implement the standards in a semantic integration hub Deliver services to support drug discovery programs in pharma and public domain 22 partners, 8 pharmaceutical companies, 3 biotechs 36 months project, through March 2014 Guiding principle is open access, open usage, open source - Key to standards adoption - 33. 34. Outreach and adoption activities Education and training Ontology and controlled vocabulary expertise Relationships with vendors/data providers Programming & technical support Understand data structure Libraries Libraries are supporting (& contributing!) to work areas in a variety of ways related to core mission and service areas 35. Tools & Apps. Search Visualizations Work efficiencies Analysis and evaluation 36. Search VIVOsearch and CTSAsearch VIVOsearchlight AgriVIVO FAO of the UN Search across Land Grant institutions CTSA Consortium Schools State university systems; Big 10, Big 12, etc. 37. @mileswortho 38. Visualizations! @hackerceo Inter-InstitutionalCollaborationExplorer 39. Make work easier 40. SPARQL Query Builder 41. Are you using Linked Open Data? What are your hopes for this collection of technologies? How can you get involved? 42. Open data, open tools, open process Thank you! Acknowledgements: Carlo Torniai & Melissa Haendel OHSU Tony Williams OpenPHACTS, RSC CTSA Research Networking Affinity workgroup VIVO Project