20120718 linkedopendataandnextgenerationsciencemcguinnessesip final

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Linked Data and Semantic Technologies can support a next generation of science. This talk shows examples of discovery, access, integration, analysis, and shows directions towards prediction and vision.

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20120718 linkedopendataandnextgenerationsciencemcguinnessesip final

  1. 1. Linked Open Data and Next Generation Science Deborah L. McGuinness Tetherless World Senior Constellation Chair Professor of Computer and Cognitive Science Rensselaer Polytechnic Institute, Troy, NY & CEO McGuinness Associates, Latham, NY Earth System Information Partners, Madison Wisconsin, July 18, 2012
  2. 2. Background I– Access to data is exploding with open government data and numerous agencies publishing and providing services access or at least FOIA access– Citizen interest and contributions are increasing – data gathering (e.g., bird observations), reviewing (e.g., galaxy zoo), compute cycles (e.g., SETI), …– Arguably the more large (both data volume and area breadth) science problems need addressing – these go beyond what a single research team can easily solve
  3. 3. Background II– Semantic Technologies – technological support for encoding meaning in a form computers can understand and manipulate – are maturing and increasing in usage– Computational encodings of meaning can be used to help integrate, link, validate, filter,…. Essentially to make smarter, more context-aware applications– Semantic Technologies enable linking data … and linked data provides a way of connecting and traversing information, nodes, graphs, webs, …
  4. 4. Take Home Message (early)– Linked Data is usable now by any project– Linked Data and Semantic Technologies can help in forming and connecting help large, distributed, evolving efforts such as many earth and space science projects– In the rest of talk:– Brief intro to Linked Data and Semantic Technologies through examples– Discussion about what we might do now and strive for in the future
  5. 5. Linked Data• Linked Data is quite simple and follows principles set out by Berners-Lee in http://www.w3.org/DesignIssues/LinkedData.html – Use URIs as names for things – Use HTTP URIs so that people can look up those names. – When someone looks up a URI, provide useful information, using the standards (RDF*, SPARQL) – Include links to other URIs. so that they can discover more things. – Introduction by examples and then discussion
  6. 6. Population Sciences Grid Goals• Convey complex health-related information to consumer and public health decision makers for community health impact• Inform the development of future research opportunities effectively utilizing cyberinfrastructure for cancer prevention and controlMcGuinness, D. Shaikh, A., Lebo, T, Ding, L., Courtney, P., McCusker, J., Moser,. Morgan, G.D., Tatalovich, Z., Willis, G., Contractor, N., and Hesse, B.2012. Towards Semantically-Enabled Next Generation Community Health Information Portals: The PopSciGrid Pilot In Proceedings of HawaiiInternational Conference on System Sciences 2012 6
  7. 7. Semantic Web Perspective on Initial Project Goals• How can semantic technologies be used to integrate, present, and analyze data for a wide range of users?• Can tools allow lay people to build their own demos and support public usage and accurate interpretation?• How do we facilitate collaboration and “viral” applications?• Within PopSciGrid: – Which policies (taxation, smoking bans, etc) are correlated with health and health care costs? – What data should be displayed to help scientists and lay people evaluate related questions? – What data might be presented so that people choose to make (positive) behavior changes? – What does the data show? why should someone believe that? – What are appropriate follow up questions to support actionability? 7
  8. 8. What is an Ontology? Thesauri “narrower Formal Frames GeneralCatalog/ term” is-a (properties) LogicalID relation constraints Informal Formal Value Disjointness Terms/ instance Restrs. , Inverse, glossary is-a part-of…Ontologies Come of Age McGuinness, 2001, and From AAAI Panel 99 – McGuinness, Welty, Uschold,Gruninger, LehmannPlus basis of Ontologies Come of Age – McGuinness, 2003
  9. 9. Inference Web: Making Data Transparent and Actionable Using Semantic Technologies• How and when does it make sense to use smart system results & how do we interact with them? (Mobile) Knowledge Intelligent Provenance in Virtual Agents NSF Interops: Observatories SONET SSIII – Sea Ice Intelligence Analyst Tools Hypothesis Investigation / Policy Advisors 9
  10. 10. Foundations: Web Layer Cake Visualization APIs S2S Govt Data Inference Web, Proof Markup Language, W3C Inference Web IW Trust, Provenance Working Air + Trust group formal model, W3C incubator group, DL, KIF, CL, N3Logic … Ontology repositoriesOWL 1 & 2 WG Edited main OWL (ontolinguag), Docs, quick reference, Ontology Evolution env: OWL profiles (OWL RL), Chimaera, Earlier languages: DAML, Semantic eScience DAML+OIL, Classic Ontologies, MANY other ontologie RIF WG AIR accountability tool SPARQL WG, earlier QL – OWL-QL, Classic’ QL, … Govt metadata search Linked Open Govt Data SPARQL to Xquery translator RDFS materialization (Billion triple winner) Transparent Accountable Datamining Initiative (TAM
  11. 11. PopSciGrid Workflow Ban coverage Publish CSV2RDF4LOD Direct visualize derive deriveCHSI 2009 archive Archive SemDiff CSV2RDF4LOD derive Enhance
  12. 12. PopSciGrid Example State ViewExtensible Mashups via Linked Data Diverse datasets from NIH Potentially linking to other content (e.g.“unemployment rate”)Accountable Mashups via Provenance Annotate datasets used in demos 13 Feedback users’ comment to gov contact (e.g. %) Annotation capabilities coming (and more)
  13. 13. PopSciGrid II
  14. 14. ReflectionsSuccessful but….• What if we could allow data experts to build their own demos?• What if we could allow non-subject matter experts to function as subject-literate staff?• What if team members could interchange roles (and thus make contributions in other areas)?• What technological infrastructure is required?• Claim: all of this is being done now – and it is starting to scale and growing more accessible 15
  15. 15. Updates and Motivations from a Computer Science PerspectiveOld: New:• Raw conversions • Enhanced conversions• Per-dataset vocabularies • Vocabulary reuse• Custom queries • Generic queries• Custom data • Re-usable data management code management code• Limited use because of • Unlimited use of new Google Visualization open source visualization licenses toolkit• State-level data • State and county-level data 16
  16. 16. County average life expectancy(Summary Measures of Health
  17. 17. Why Did I Show A Population Science Project and a Water Project?Questions and goals are similar – What’s happening with x? – health of a country, water quality and other parts of an ecosystem, climate changes What intervention strategies are being tested What policies are correlated with factors under investigation And Why should people believe the outcome?
  18. 18. See Global Change Provenance Representation in the Global Change Information System (GCIS) Curt.Tilmes@nasa.govWhat’s happening with the climateand how will it affect the U.S.?National Climate Assessment 201330 chapters, 240 authorsA “Highly Influential Scientific Assessment”Why should I believe it?GCIS presenting the provenance of the reportitself, the key messages of the report,including traceable accounts of the >500technical inputs from reports, papers, models,datasets, observations, etc.
  19. 19. SemantEco/SemantAqua• Enable/Empower citizens & scientists to explore pollution sites, facilities, regulations, and health impacts along with provenance. 5 4• Demonstrates semantic 2 3 monitoring possibilities.• Map presentation of analysis• Explanations and Provenance 1 available http://was.tw.rpi.edu/swqp/map.html and 1. Map view of analyzed results http://aquarius.tw.rpi.edu/projects/semantaqua 2. Explanation of pollution 3. Possible health effect of contaminant (from EPA) 4. Filtering by facet to select type of data 5. Link for reporting problems 6. Now joint with USGS resource managers ; expanded to endangered species; now more virtual observatory style
  20. 20. System ArchitectureVirtuoso access 21
  21. 21. Originally developed for VSTO, now in SSIII, SESDI, SESF, OOI … The Virtual Solar-TerrestrialObservatory: A Deployed Semantic Web Application Case Study for Scientific Research. Proc. 19Conf. on Innovative Applications of Artificial Intelligence (IAAI-07), http://www.vsto.org
  22. 22. Reflections• What began as Semantic water quality monitoring is now SemantEco – ecological and environmental monitoring in support of ecosystem analysis• Now includes endangered species and related health impacts working with USGS to prototype resource manager dashboard• Expanding to include citizen science reporting on water on mobile platforms• Now working with SONet, Santa Barbara County LTER, CUASHI to integrate other related scientific observations – Current focus use case ecological researcher – Find relevant data (within and outside DataOne) by region, timeframe, chemical, measurement dimension, species – Currently background ontology is relatively simple and aims more at discovery and integration• Semantic Sea Ice project aimed at helping arctic ice researchers find and evaluate data in support of understanding the state of ice in the arctic• These technologies span the spectrum of supporting discovery, integration, 23 analysis, and ultimately prediction
  23. 23. Discussion• Semantic Technologies and Linked Data are powering a wide array of applications – many in Big Science, Team Science, at least interdisciplinary science• Tools and methodologies are ready for use• We love to partner in these areas• What do you need or want from linked data and semantic technologies?Questions? - Deborah McGuinness dlm @ cs . rpi . edu
  24. 24. Extra
  25. 25. RDF Data Cube Vocabulary • Integrated with the LOGD• For publishing multi- data conversion dimensional data, such infrastructure as statistics, on the web in such a way that it can • Integrated with other tooling be linked to related data like Stats2RDF sets and concepts using RDF.• Compatible with the cube model that underlies SDMX (Statistical Data and Metadata eXchange).• Also compatible with: – SKOS, SCOVO, VoiD, FOAF, Dublin Core Terms 26
  26. 26. Foundations: The Tetherless World Constellation Linked Open Government Data Portal Convert TWC LOGD Query/ Access LOGD Community Portal SPARQL • RDF Endpoint • RSS • JSONCreate • XML • HTML • CSV •… Enhance Data.gov deployment 27
  27. 27. Directions• Incorporation of TWC data Quality Facts label (Zednik et al)• Use of DataFAQs automated data quality framework (Lebo et al)• Additional provenance inclusion / usage (Inference / Provenance Web)• Annotation / Collaboration facilities (Michaelis et al)• Other data sets? Or exposition of other parameters?• Partners in additional topic areas 28

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