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Resource Description Framework Approach to Data Publication and Federation

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Bob Stanley, CEO, IO Informatics, explains the utility to RDF as a standard way of defining and redefining data that could have utility in managing life science information.

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Resource Description Framework Approach to Data Publication and Federation

  1. 1. Resource Description Framework Approach to Data Publication and Federation Robert Stanley IO Informatics, Inc. IO Informatics © 2011 June 6 2011 Pistoia Alliance – Open Technical Committee Meeting
  2. 2. Agenda <ul><li>Introduction: </li></ul><ul><ul><li>Requirements, Background, Use Cases </li></ul></ul><ul><li>Technical Example / Use Case: </li></ul><ul><ul><li>Requirements for creating a data service and invoking a query </li></ul></ul><ul><li>Q & A </li></ul>
  3. 3. Recap - ELN Query Functional Requirements <ul><li>The ELN Query Services team have produced a rich set of functional requirements/user stories representing common questions scientists have that can be satisfied with an ELN query. Most, if not all, of those requirements have the conceptual form; </li></ul><ul><li>SELECT <selection> FROM Experiment WHERE <constraints> </li></ul><ul><li>… A common approach to the problem would be preferable , ideally leveraging existing standards, rather than building a solution that works just for Experiment. </li></ul><ul><li>… the Pistoia Technical Committee may wish to constrain such query services, by aligning to existing standards, to ensure consistency in approach. </li></ul>
  4. 4. Example ELN Query Workflow <ul><li>Scientist researching a class of Agents </li></ul><ul><ul><li>small molecules (or biologics) intended to hit a target or targets </li></ul></ul><ul><ul><li>links to… </li></ul></ul><ul><li>Assays </li></ul><ul><ul><li>test to determine activity, affinity, binding, promiscuity </li></ul></ul><ul><ul><li>determine potential toxicity, adverse events, etc. </li></ul></ul><ul><ul><li>links to… </li></ul></ul><ul><li>Targets </li></ul><ul><ul><li>sites where compounds bind -- can be locations on a protein, locations on a gene, active centers on an enzyme, etc. </li></ul></ul><ul><ul><li>links to… </li></ul></ul><ul><li>Disease/ Gene relationships </li></ul><ul><ul><li>e.g. biology, can be from TMO / LODD resources </li></ul></ul><ul><ul><li>pathways, proteins, catalysts, immunology defense mechanisms, potential for adverse events, etc. can be included </li></ul></ul>
  5. 5. Recap - Query Service API <ul><li>As part of the phase two deliverable, the ELN Query Services team produced a prototype SOAP-based service outlining the methods that would be required to support the query service. </li></ul><ul><li>There are existing protocols and standards for querying structured data over the web. Aligning to an existing approach will prevent re-inventing a query language and will provide confidence in the stability of the query interface. Examples; </li></ul><ul><li>OData (http://www.odata.org/) is a RESTful query protocol … </li></ul><ul><li>GData (http://code.google.com/apis/gdata/). Very similar to OData, but published by Google… </li></ul><ul><li>SPARQL (http://www.w3.org/TR/rdf-sparql-query/) is a RDF query protocol published by W3C for querying linked data. A triple store is [not ] required and there may be synergies with existing Pistoia projects (e.g. VSI, SESL) by adopting SPARQL. </li></ul>
  6. 6. Questions for Tech Committee <ul><li>Ontology content and format </li></ul><ul><ul><li>Regarding reference content </li></ul></ul><ul><ul><ul><li>How comprehensive and refined does it need to be? </li></ul></ul></ul><ul><ul><ul><li>How can it relate to records and different detail? </li></ul></ul></ul><ul><ul><ul><li>Who’s going to maintain the ontology? </li></ul></ul></ul><ul><ul><ul><li>Is there a practical, agile approach to creating, managing, extending? </li></ul></ul></ul><ul><ul><li>Standards - driven? (UML, OWL, or ERD, etc.) </li></ul></ul><ul><ul><li>Investigate service versioning and how backward compatibility might be implemented </li></ul></ul><ul><li>API Mechanism (OData, Gdata, SPARQL?) </li></ul><ul><ul><li>Is there a standards-based approach? </li></ul></ul>
  7. 7. Moving Forward? Resource Description Framework <ul><li>Semantic Technologies provide a unified, standard framework for: </li></ul><ul><ul><li>Ontology / API representation, modification, merging </li></ul></ul><ul><ul><li>Query framework / SOA SPARQL API </li></ul></ul><ul><ul><li>Rich, extensible Query Federation </li></ul></ul><ul><ul><ul><li>(Does not require ETL) </li></ul></ul></ul><ul><li>Agile ontology development & maintenance… </li></ul><ul><ul><ul><li>Customers can extend the ontologies themselves </li></ul></ul></ul><ul><li>Broadly – these are standards-driven methods that will make the ELN Query Federation lifecycle much easier </li></ul>
  8. 8. What is RDF? <ul><li>Resource Description Framework (“RDF”) defines and links data for more effective discovery, federation, integration and re-use across applications </li></ul><ul><li>RDF is a fundamentally simple, standard and extensible way of specifying data and data relationships </li></ul><ul><li>Ontologies describe resources and relationships according to their explicit meaning </li></ul><ul><li>SPARQL Protocol and RDF Query Language (“SPARQL”) supports federated queries w’ SPARQL API for publication </li></ul>
  9. 9. <ul><li>RDF graphs are collections of triples </li></ul><ul><li>Triples are made up of a subject , a predicate , and an object </li></ul><ul><li>Resources and relationships (metadata)are stored </li></ul>Resource Description Framework (RDF) is… A labeled, directed graph of relations between resources and literal values. Confidential IO Informatics © 2011 subject object predicate
  10. 10. <ul><li>“ TP53 encodes Human p53” </li></ul><ul><li>“ p53 is a tumor suppressor protein” </li></ul><ul><li>“ TP53 gene is located on the short arm of chromosome 17” </li></ul>Example RDF Triples Confidential TP53 p53 encodes p53 tumor suppressor protein is a TP53 located chromosome 17
  11. 11. Triples Connect to Form Graphs TP53 Confidential p53 encodes tumor suppressor protein is a located chromosome 17 part of Human part of
  12. 12. Why RDF? What’s Different Here? <ul><li>Triples act as a human and machine readable least common denominator for expressing data and relationships </li></ul><ul><li>Ontologies organize data according to their human readable meaning, to make defining and merging data intuitive… </li></ul><ul><li>RDF supports inference and disambiguation , so merging data and adding new data and relationships without shared identifiers becomes possible… </li></ul>Confidential IO Informatics © 2011
  13. 13. Maturation of a Standard Framework for Resource Description <ul><li>This standard method and framework for extensible open data publication is maturing: </li></ul><ul><ul><li>Standards and practices, ontologies, query and data model specification: </li></ul></ul><ul><ul><ul><li>W3C, ISCB, IEEE, NCBO, OBO </li></ul></ul></ul><ul><ul><ul><li>SKOS, LOD / LODD and related resources </li></ul></ul></ul><ul><ul><li>Federation methods: </li></ul></ul><ul><ul><ul><li>Ontology Resources, URIs, SPARQL endpoints, ontology alignment, inference, D2R, SWObjects, … </li></ul></ul></ul><ul><ul><li>Scalability: </li></ul></ul><ul><ul><ul><li>Security, support for transactional processing </li></ul></ul></ul><ul><ul><li>Practice: </li></ul></ul><ul><ul><ul><li>Expertise, training, larger projects (FDA, DOD, NASA, World Cup, Data.gov, Chevron, etc.) </li></ul></ul></ul>IO Informatics © 2011
  14. 14. Exponentially Growing Resources 2007 2008 2009 2011 IO Informatics © 2011
  15. 15. Healthcare / Life Sciences remains the largest data sector for SPARQL APIs
  16. 16. Integration / Federation Options Data Sources RDBMS / REST / Web Services Basic Transformation Query-Based Transformation SPARQL 2 SQL (D2R, SWObjects) Federated Semantic Access / Datastore(s) SPARQL APIs / SOAP / REST Provenance Versioning Governance Meaning
  17. 17. RDF / SPARQL Solution Stack DBs, Services SPARQL API, SPARQL conversion, ETL=>RDF Extensible Standards-based Framework Federated Access / Integrated Semantic Datastore(s) Prediction and Simulation Apps Query by Meaning Rich Browsing Other Data Sources Public Data, Services Other Apps
  18. 18. Example Use Cases <ul><li>Manufacturing: Link data sources across imprecise connections to verify reports </li></ul><ul><li>Animal Safety: Knowledge Network for discovery, qualification and validation of cross-species biomarkers </li></ul><ul><li>Personalized Medicine: Knowledge Network and screening application for personalized medicine </li></ul>IO Informatics © 2011
  19. 19. Example Questions <ul><li>What data sources support this specific manufacturing report about product purity and shelf-life? </li></ul><ul><li>What toxicity biomarkers are common to most animals? </li></ul><ul><li>What patients are showing combinations of indicators that predict risk? </li></ul>IO Informatics © 2011
  20. 20. Qualitative Benefits <ul><li>Link internal data with public resources </li></ul><ul><ul><li>E.G. – “out of box” linking of ELN data with LODD sources </li></ul></ul><ul><ul><li>Growing public resources provide cost-effective enrichment and hypothesis generation </li></ul></ul><ul><ul><li>Reduce dependencies on expensive commercial databases </li></ul></ul><ul><li>Emergent Properties </li></ul><ul><ul><li>ELN data enrichment and knowledge building made easy! </li></ul></ul><ul><ul><li>Supports inference, rich interrogation, serendipitous discovery </li></ul></ul><ul><li>R&D concepts can now be translated to immediate consumer benefits </li></ul><ul><ul><li>Previous “out of reach” integration and applications become practical </li></ul></ul>IO Informatics © 2011
  21. 21. Quantitative Benefits <ul><li>Reduced effort, time and cost to federate, update, extend to new datasets </li></ul><ul><li>Start sooner - reduce initial design and deployment burden </li></ul><ul><ul><li>Ontologies are explicit, can be engineered from or decoupled from resources, can be altered without refactoring </li></ul></ul><ul><li>Finish sooner - reduced time to create and test integrations </li></ul><ul><ul><li>Agile modeling, integration and testing </li></ul></ul><ul><li>Extend more easily - add new data sources and applications </li></ul><ul><ul><li>Building blocks to add new data, create new applications </li></ul></ul><ul><ul><li>End Users can adapt, modify, extend ontologies </li></ul></ul>IO Informatics © 2011
  22. 22. UBC: Knowledge Network for Organ Failure; “ASK” for Personalized Medicine IO Informatics © 2011
  23. 23. UBC: Knowledge Network for Organ Failure; “ASK” for Personalized Medicine <ul><li>Outcome </li></ul><ul><ul><li>Knowledge network for enrichment, visualization and qualification of patterns indicating risk of organ failure </li></ul></ul><ul><ul><li>Web-based deployment of SPARQL-based screening patterns across multiple data sources, indicating patients-at-risk </li></ul></ul>IO Informatics © 2011
  24. 24. <ul><li>Screening of transplant patients for likelihood of transplant failure, based on combined biomarker patterns </li></ul>Personalized Medicine Knowledge Network IO Informatics © 2011 <ul><li>Web-based Knowledge Application </li></ul><ul><li>Applies patterns for predictive screening </li></ul><ul><li>Weighing, scoring of results </li></ul><ul><li>Bring “hits” back into Knowledge Network for validation of hypotheses and algorithms </li></ul>
  25. 25. ASK™ <ul><li>Extended SPARQL-based Knowledge Application </li></ul><ul><li>Visualization, Testing, Validation of Systems-oriented Hypotheses </li></ul><ul><li>Automation, Dashboards </li></ul>Confidential IO Informatics © 2011
  26. 26. UBC / PROOF: Quantitative… <ul><ul><li>Integration and analysis time reduced from estimated 2 years to about 8 months FTE equivalent </li></ul></ul><ul><ul><li>Time to capture and apply patterns reduced from days to hours </li></ul></ul><ul><ul><li>Knowledge base can be / has been extended to include new public sources in hours (days / weeks with curation) </li></ul></ul><ul><ul><ul><li>Expensive commercial database no longer needed due to ease of integrating public resources </li></ul></ul></ul>IO Informatics © 2011
  27. 27. UBC / PROOF: Qualitative… <ul><ul><li>Visual SPARQL presents queries as hypotheses </li></ul></ul><ul><ul><ul><li>Make it possible for researchers to iteratively create, test and refine hypotheses </li></ul></ul></ul><ul><ul><ul><li>Research queries were published to web service for easy scale-up </li></ul></ul></ul><ul><ul><ul><li>Extended SPARQL delivers practically useful classifiers with scoring </li></ul></ul></ul><ul><ul><li>Use of RDF makes enrichment with public sources practical </li></ul></ul><ul><ul><li>Provenance, reference annotation, original data accessible for review </li></ul></ul><ul><li>“ [The] ability to consume and intuitively represent a wide variety of data-types - from images to quantitative data - and more importantly, display that data in ways that make the significant features immediately obvious to our biologist end-users, has allowed us to move to a completely new level of data analysis….” </li></ul>IO Informatics © 2011
  28. 28. Recap – Core Benefit Drivers <ul><li>Semantic technologies provide a standard method and framework for data publication and interoperability… not a new “data standard”! </li></ul><ul><li>Low barrier to entry for data publishing – extensible building blocks </li></ul><ul><li>for agile, growing integrations </li></ul><ul><li>Reduced effort, time and cost to deliver and maintain data </li></ul><ul><li>definitions and applications, particularly those that depend on </li></ul><ul><li>federation </li></ul><ul><li>Growing public resources are a catalyst and value-add </li></ul><ul><li>Projects that were impractical become practical to achieve and </li></ul><ul><li>maintain </li></ul>IO Informatics © 2011
  29. 29. Thank you! Next, W3C… For questions: <ul><li>Email: [email_address] </li></ul><ul><li>Website: www.io-informatics.com </li></ul>IO Informatics © 2011

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