1. Solar Terrestrial Ontologies – in Support of Virtual Observatories and Large Scale Semantic Scientific Data Integration Deborah McGuinness Co-Director and Senior Research Scientist, Knowledge Systems. AI Laboratory Stanford University dlm@ksl.stanford.edu http://www.ksl.stanford.edu/people/dlm CEO McGuinness Associates Joint work with Peter Fox 2, Don Middleton 3, Luca Cinquini 3, James Benedict 1, Tony Darnell 2, Jose Garcia 2,, Patrick West 2, 1 McGuinness Associates 2 High Altitude Observatory, NCAR 3 Scientific Computing Division, NCAR Partially funded by NSF (Computer and Information Science and Engineering (CISE) in the Shared Cyberinfrastructure (SCI) division)

2. May 11, 2006 Deborah L. McGuinness 2 Key Enabler – The Semantic Web Ontology Level –Languages (CLASSIC, DAML-ONT, DAML+OIL, OWL, …) –Environments (FindUR, Chimaera, OntoBuilder/Server, Sandpiper …) –Standards (NAPLPS, …, W3C’s WebOnt, W3C’s Semantic Web Best Practices, EU/US Joint Committee, OMG ODM, …) Rules –SWRL (previously CLASSIC Rules, …) Logic –Description Logics Proof –PML, Inference Web Services and Infrastructure Trust –IWTrust, Collaborative Information Repository Trust, NSF TAMI with W3C/MIT Applications –VSTO, SESDI, SKIF, SSOA, BISTI, … –Domain ontologies & environments –Tools academic & industry – sandpiper/cerebra/… http://www.w3.org/2004/Talks/0412-RDF-functions/slide4-0.html

3. May 11, 2006 Deborah L. McGuinness 3 Virtual Observatories Scientists should be able to access a global, distributed knowledge base of scientific data that: appears to be integrated appears to be locally available But… data is obtained by multiple instruments, using various protocols, in differing vocabularies, using (sometimes unstated) assumptions, with inconsistent (or non- existent) meta-data. It may be inconsistent, incomplete, evolving, and distributed

4. May 11, 2006 Deborah L. McGuinness 4 Virtual Observatory Defined Workshop: A Virtual Observatory (VO) is a suite of software applications on a set of computers that allows users to uniformly find, access, and use resources (data, software, document, and image products and services using these) from a collection of distributed product repositories and service providers. A VO is a service that unites services and/or multiple repositories. VxOs - x is one discipline

5. May 11, 2006 Deborah L. McGuinness 5 Virtual Observatories in Practice Make data and tools quickly and easily accessible to a wide audience. Operationally, virtual observatories need to find the right balance of data/model holdings, portals and client software that a researchers can use without effort or interference as if all the materials were available on his/her local computer using the user’s preferred language. They are likely to provide controlled vocabularies that may be used for interoperation in appropriate domains along with database interfaces for access and storage and “smart” search functions and tools for evolution and maintenance.

6. May 11, 2006 Deborah L. McGuinness 6 Virtual Solar Terrestrial Observatory (VSTO) A distributed, scalable education and research environment for searching, integrating, and analyzing observational, experimental, and model databases. Integrates data sets using declarative shared schema definitions – the Solar Terrestrial Ontology Domains include: solar, solar-terrestrial and space physics It provides virtual access to specific data, model, tool and material archives containing items from a variety of space- and ground-based instruments and experiments, as well as individual and community modeling and software efforts bridging research and educational use VSTO addresses the interdisciplinary metadata and ontology problem - bridging terminology and use of data across disciplines VSTO leverages the development of schema that adequately describe the syntax (name of a variable, its type, dimensions, etc. or the procedure name and argument list, etc.), semantics (what the variable physically is, how it is related to other objects, etc.) and pragmatics (what the procedure does and returns, etc.) of the datasets and tools. VSTO provides a basis for a framework for building and distributing advanced data assimilation tools

7. May 11, 2006 Deborah L. McGuinness 7 Content: Coupling Energetics and Dynamics of Atmospheric Regions WEB Community data archive for observations and models of Earth's upper atmosphere and geophysical indices and parameters needed to interpret them. Includes browsing capabilities by periods, instruments, models, …

8. May 11, 2006 Deborah L. McGuinness 8 Content: Mauna Loa Solar Observatory Near real-time data from Hawaii from a variety of solar instruments. Source for space weather, solar variability, and basic solar physics Other content used too – CISM – Center for Integrated Space Weather Modeling

9. May 11, 2006 Deborah L. McGuinness 9 Selected Ontology Topics Instrument Ontology –Instrument classes are leveragable across broad areas. –Developed for Solar-Terrestrial Domain… but in a SESDI project volcano ontology workshop, we revealed significant reuse potential Parameters Meta-data focus

10. May 11, 2006 Deborah L. McGuinness 10 Selected Use Case –Ontology-enhanced search (initially for CEDARWEB and appropriately interconnected data portals) –Retrieve data (using a semantically richer set of background knowledge to help eliminate errors or queries that do not make sense) –What can I plot (x vs. y based on semantics) May be rephrased as “what makes sense to plot” –Enhanced plotting using understanding of coordinate systems, relationships, data synthesis, transformations, etc. “Retrieve data subject to the following conditions… and plot it in a way that makes sense for the data”

11. May 11, 2006 Deborah L. McGuinness 11 One (Domain-Specific) Example Use Case Find data which represents the state of the neutral atmosphere anywhere above 100km and toward the arctic circle (above 45N) at any time of high geomagnetic activity. Use the data from the CEDAR database above 100km defines the wavelength operating interval high geomagnetic activity is defined by geophysical index Kp > 10 (thus we can specify values for operating interval and Kp without requiring this information from the user or query manager)

12. May 11, 2006 Deborah L. McGuinness 12

13. May 11, 2006 Deborah L. McGuinness 13 Instrument Class Excerpt OpticalInstrument Interferometer Fabry-PerotInterferometer MichelsonInterferometer IRMichelsonInterferometer DopplerMichelsonInterferometer AirglowImager AllSkyCamera Lidar Spectrophotometer Spectrometer Polarimeter Heliograph Photometer SingleChannelPhotometer MultiChannelPhotometer Taxonomy of instruments covering content areas. Currently expanding and evaluating. Advertisement – come to the ontologies for earth and space science meeting at APL on May 26 – look in more detail Approach: identify instruments & parameters organize hierarchically compare/extend SWEET (realms, properties, space, …) scientific expert review ontology expert review related scientific review populate instances (including meta- data) use-case driven

14. May 11, 2006 Deborah L. McGuinness 14 Discussion/Status Virtual Observatories are emerging (VSTO, Astrophysical, …) Scientific Data Sharing is required Ontologies can help with –Controlled vocabularies with unambiguous term meanings –Mapping/Merging support for data integration –Ontology-enhanced search –Meta-data descriptions –Consistency Checking –Completion –Structured, “surgical” comparative customized search VSTO Solar Terrestrial Ontology is available, and we believe, reusable. Evidence emerging from SESDI, extended use cases, SKIF, GEON, …going online for Mauna Loa in August Communities can help each other by pooling resources over scientific ontology creation, use, evaluation, evolution, and environment development

15. May 11, 2006 Deborah L. McGuinness 15 Discussion Virtual Observatories powered by ontologies and the semantic web are ready for use, evolution, expansion They enable a new paradigm for scientific research – one where researchers collaborate internationally in a virtual space where they can have unambiguous descriptions of data, experiments, instrument settings, assumptions, etc. There are good starting points – SWEET, VSTO, … Lets leverage each others work Come to the AGU meeting in Baltimore (Thursday May 25 sessions in particular) and the Ontology meeting at APL on May 26.

16. May 11, 2006 Deborah L. McGuinness 16 More Information Virtual Solar Terrestrial Observatory (VSTO): http://vsto.hao.ucar.edu Semantic Web for Earth and Environmental Terminology (SWEET): http://sweet.jpl.nasa.gov Coupling, Energetics and Dynamics of Atmospheric Regions (CEDAR): http://cedarweb.hao.ucar.edu Center for Integrated Space Weather Modeling (CISM): http://www.bu.edu/cism Mauna Loa Solar Observatory (MLSO): http://mlso.hao.ucar.edu W3C’s Web Ontology Language (OWL) - http://www.w3.org/TR/owl-features/http://www.w3.org/TR/owl-features/ Near term meetings: -Semantic Scientific Data Integration at AGU ‘06 on May 25, 2006 - www.agu.org/meetings/ja06/?content=search&show=detail&sessid=101 www.agu.org/meetings/ja06/?content=search&show=detail&sessid=101 -Workshop on Earth and Space Science Ontologies at Johns Hopkins Applied Physics Lab on May 26, 2006 sras.jhuapl.edu/workshop.html Deborah McGuinness dlm@ksl.stanford.edudlm@ksl.stanford.edu Peter Fox pfox@ucar.edupfox@ucar.edu Come to the Poster session!!!

17. May 11, 2006 Deborah L. McGuinness 17 Extras

18. May 11, 2006 Deborah L. McGuinness 18 Impact: Changing Science Scientists: What if you… -could not only use your data and tools but remote colleague’s data and tools? -understood their assumptions, constraints, etc and could evaluate applicability? -knew whose research currently (or in the future) would benefit from your results? -knew whose results were consistent (or inconsistent) with yours?… Funders: What if you … -could identify how one research effort would support other efforts? -(and your fundees) could reuse previous results? -(and your fundees) could really interoperate? CS: What if you had a sandbox and you … -could apply your techniques across very large distributed teams of people with related but different apps? -could compare your techniques with colleagues trying to solve similar problems?

19. May 11, 2006 Deborah L. McGuinness 19 NASA Application One trend in science: moving from instrument- based to measurement-based Requires: ‘bridging the discipline data divide’ Overall vision for SESDI: To integrate information technology in support of advancing measurement- based processing systems for NASA by integrating existing diverse science discipline and mission- specific data sources. SWEET VolcanoClimate SESDI

20. May 11, 2006 Deborah L. McGuinness 20 Semantic connectors The SESDI re-useable component interfaces. The stub on each end of the connector is based on the GEON Ontology-Data registration technology and contains articulated axioms derived from the knowledge gained in the unit-level data registration. Includes integrity checks, domain and range, etc. SWEET Process-oriented semantic content represented in SWSL ---------------------------- Articulation axioms

21. May 11, 2006 Deborah L. McGuinness 21 Instrument Class Excerpt Radar IncoherentScatterRadar DopplerRadar IonosphericDopplerRadar MiddleAtmosphereRadar MSTRadar MFRadar LFRadar MeteorWindRadar BiStaticRadar SyntheticApertureRadar PhasedArrayRadar Taxonomy of instruments covering content areas. Currently expanding and evaluating. Advertisement – come to the ontologies for earth and space science meeting at APL on May 26 – look in more detail Approach: identify instruments & parameters organize hierarchically compare/extend SWEET (realms, properties, space, …) scientific expert review ontology expert review related scientific review populate instances (including meta- data) use-case driven