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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 LaboratoryStanford University
[email protected]://www.ksl.stanford.edu/people/dlm
CEO McGuinness Associates
Joint work with Peter Fox2, Don Middleton3, Luca Cinquini3, James Benedict1, Tony Darnell2, Jose Garcia2,, Patrick West2,
1McGuinness Associates2High Altitude Observatory, NCAR
3Scientific Computing Division, NCAR
Partially funded by NSF (Computer and Information Science and Engineering (CISE) in the Shared Cyberinfrastructure (SCI) division)
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 LogicsProof
– 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
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
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
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.
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
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, …
May 11, 2006 Deborah L. McGuinness 8
Content: Mauna Loa Solar ObservatoryNear 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
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
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”
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)
May 11, 2006 Deborah L. McGuinness 12
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. Currentlyexpanding 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
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
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.
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/
• 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-Workshop on Earth and Space Science Ontologies at Johns Hopkins Applied Physics Lab on May 26, 2006sras.jhuapl.edu/workshop.html
Deborah McGuinness [email protected] Fox [email protected]
Come to the Poster session!!!
May 11, 2006 Deborah L. McGuinness 17
Extras
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?
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
Volcano Climate
SESDI
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
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. Currentlyexpanding 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