SOLAR-TERRESTRIAL ONTOLOGIES (for VSTO and Beyond)

McGuinness Geon 5/5/2005

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, NCAR2Scientific Computing Division, NCAR

3McGuinness Associates

Partially funded by NSF (Computer and Information Science and Engineering (CISE) in the Shared Cyberinfrastructure (SCI) division)


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


BackgroundScientists 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


Virtual ObservatoriesMake 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.


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


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, …


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


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


What is an Ontology?

Catalog/ID

GeneralLogical

constraints

Terms/glossary

Thesauri“narrower

term”relation

Formalis-a

Frames(properties)

Informalis-a

Formalinstance

Value Restrs.

Disjointness, Inverse, part-

of…

*based on AAAI ’99 Ontologies panel – McGuinness, Welty, Ushold, Gruninger, Lehmann


Semantic Web LayersOntology 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


Instrument Class Excerpt• 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. Currentlyexpanding 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 meta-data)• use-case driven


Current Technology Focus

• 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.


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


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


Impact: Changing ScienceScientists: 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?


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] McGuinness [email protected]

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SOLAR-TERRESTRIAL ONTOLOGIES (for VSTO and Beyond)