Transcript The VIRTUAL SOLAR-TERRESTRIAL OBSERVATORY

SOLAR-TERRESTRIAL ONTOLOGIES
(for VSTO and Beyond)
Peter Fox1, Deborah McGuinness3, Don
Middleton2, Stan Solomon1, Jose Garcia1, Luca
Cinquini2, Patrick West1, James Benedict3
1High
Altitude Observatory, NCAR
2Scientific Computing Division, NCAR
3McGuinness Associates
Partially funded by NSF (Computer and
Information Science and Engineering (CISE) in
the Shared Cyberinfrastructure (SCI) division)
McGuinness Geon 5/5/2005
Outline
• Problem:
– Sharing (Solar-Terrestrial) Scientific Data
• Problem Setting: Virtual Observatories
– Virtual Solar-Terrestrial Observatory Project
• Solution Strategy:
– Ontologies (providing a controlled vocabulary with
unambiguous machine operational definitions)
– Ontology-enabled tools
– Connect /Extend /Validate complementary terminologies
• Technology Status
• Conclusion / Pointers
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Background
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 nonexistent) meta-data. It may be inconsistent,
incomplete, evolving, and distributed
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Virtual Observatories
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” tools for evolution
and maintenance.
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Virtual Solar Terrestrial Observatory
(VSTO)
• a distributed, scalable education and research
environment for searching, integrating, and analyzing
observational, experimental, and model databases.
• subject matter covers the fields of 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
• 3 year NSF-funded project in first year
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Content: Coupling Energetics and Dynamics
of Atmospheric Regions WEB
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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, …
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
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VSTO Ontologies
• Technology for encoding meaning of terms supporting
interoperation across applications, education, reasoning,
etc.
• Beginning with common, high-leverage terminologies
– ***Instruments
– ***Parameters
– Sun Realm …
• Integrating with/extending Semantic Web for Earth and
Environmental Terminology, GEON, …
• Encoding in W3C’s OWL
• Initial Use – Ontology-enhanced search and “smarter”
portals
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What is an Ontology?
Catalog/
ID
Thesauri
“narrower
term”
relation
Terms/
glossary
Frames General
Formal
is-a (properties) Logical
constraints
Informal
is-a
Formal
instance
Disjointness,
Value Inverse, partRestrs. of…
*based on AAAI ’99 Ontologies panel – McGuinness, Welty, Ushold, Gruninger, Lehmann
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Semantic
Web
Layers
Ontology Level
– Language (OWL (RDF/XML compatible))
– Environments (inspired by FindUR, Chimaera, Ontolingua,
OntoBuilder/Server, Sandpiper Tools, Cerebra, …)
– Standards body leverage (W3C’s WebOnt, W3C’s Semantic Web
Best Practices, EU/US Joint Committee, OMG ODM, Scientific
Markup Standards, …)
Rules
– SWRL
Logic
– Description Logics
Proof
– PML, Inference Web Services and
Infrastructure
Trust
– IWTrust
http://www.w3.org/2004/Talks/0412-RDF-functions/slide4-0.html
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Instrument Class Excerpt
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Radar
Incoherent Scatter
Ionospheric Doppler(aka HF)
Middle Atmosphere (aka MLT)
MST
MF
LF
Meteor Wind
Digisondes
Optical (hasBand, measuresTo, etc.)
Interferometers
Fabry-Perot
Michelson
IR
Doppler
Spectrometers
IR ([OH])
Airglow Imagers
All-Sky Cameras
Lidar
Spectrometers
Polarimeter
Heliograph
Photometers
Single-Channel
Multi-Channel
Taxonomy of instruments
covering content areas. Currently
expanding and evaluating.
COMMENTS Welcome!!!!
Approach:
• identify instruments & parameters
• organize hierarchically
• compare/extend SWEET (realms,
properties, space, …)
• scientific expert review
• ontology expert review
• related scientific review
• populate instances (including metadata)
• use-case driven
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Current Technology Focus
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Instruments
Parameters
Meta-data
Use-Case
– ** Ontology-enhanced search (initially for
CEDARWEB and appropriately interconnected data
portals)
– What can I plot (x vs. y based on semantics)
– Enhanced plotting using understanding of coordinate
systems, relationships, data synthesis,
transformations, etc.
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Primary Integration Areas
• Base Ontology: SWEET with extensions. This
provides both validation for SWEET/GEON as
well as content extensions
• Virtual Observatory with its tool infrastructure
– Scientific ontology-enabled search (note spectrum of
options)
– Meta data vocabulary, registry for instruments, data
sets, etc.
• Use Case Analysis
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Conclusion I
• 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 and GEON are natural/complementary partners
• Communities can help each other by pooling resources over
scientific ontology creation, use, evaluation, evolution, and
environment development
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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
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problems?
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/
Peter Fox [email protected]
Deborah McGuinness [email protected]
See the Poster !!!
McGuinness Geon 5/5/2005