Ontologies in Practice - a Solar Terrestrial Example

Transcription

1 Ontologies in Practice - a Solar Terrestrial Example Peter Fox1 (pfox@ucar.edu) and Deborah McGuinness23 (dlm@cs.stanford.edu) 1 HAO/ESSL/NCAR 2 Stanford Knowledge Systems, AI Lab 3 McGuinness Associates NSF and NASA funded

2 Definitions Semantic Web An extension of the current web in which information is given welldefined meaning, better enabling computers and people to work in cooperation, Primer: Semantic Grid Semantic services to use the resources of many computers connected by a network to solve large scale computational problems Ontology (n.d.). An explicit formal specification of how to represent the objects, concepts and other entities that are assumed to exist in some area of interest and the relationships that hold among them. The Free On-line Dictionary of Computing. DL - Description Logic (dl.kr.org/ ) FOL - First Order Logic RDF - Resource Description Framework W3C recommendation OWL - Ontology Web Language (Lite, DL, Full) W3C Recommendation

3 Representing Knowledge Introduction to the idea of ontologies using a scientific use case How they are being used/ implemented Comments about the community effort involved in building ontologies. The Virtual Solar-Terrestrial Observatory (VSTO) addresses the interdisciplinary metadata and ontology problem - bridging terminology and use of data across disciplines leverages the development of schema describing syntax (name of a variable, its type, dimensions, etc. or the procedure name and argument list, etc.), semantics (what the variable physically is, its units, etc.) and pragmatics (or what the procedure does and returns,its uses, etc.) of the datasets and tools.

4 Integrative use-case: General: Find data subject to constraints and optionally plot in a way that makes sense for the data Specific Example: Find data representing the state of the neutral atmosphere anywhere above 100km and toward the arctic circle (above 45N) at any time of high geomagnetic activity. How to translate this into a complete query for data? What information do we have to extract from the use-case? What information we can infer (and integrate)? Most of all - what will be returned? Data from instruments, indices and models!!!

5 Translating the Use-Case - non-monotonic? GeoMagneticActivity has ProxyRepresentation Input GeophysicalIndex is a ProxyRepresentation (in Physical properties: State of Realm of Neutral Atmosphere) neutral atmosphere Kp is a GeophysicalIndex Spatial: hastemporaldomain: daily Above 100km hashighthreshold: Toward arctic circlexsd_number = 8 (above 45N) Date/time when KP => 8 Conditions: Specification needed for query to CEDARWEB Instrument Parameter(s) Operating Mode Observatory Date/time High geomagnetic activity Action: Return Data Return-type: data

6 NeutralAtmosphere is a subrealm of TerrestrialAtmosphere Translating the Use-Case hasphysicalproperties: NeutralTemperature, Neutral Wind, etc. monotonic? hasspatialdomain: [0,360],[0,180],[100,150] Specification needed for Input hastemporaldomain: query to CEDARWEB Physical properties: State of NeutralTemperature Instrument neutral atmosphereis a Temperature (which) is a Parameter Spatial: Above 100km GeoMagneticActivity has ProxyRepresentation Toward arctic circle (above 45N) GeophysicalIndex is a ProxyRepresentation (in Conditions: Realm of Neutral Atmosphere) High geomagnetic Kp is a GeophysicalIndex activity hastemporaldomain: daily Action: Return Data hashighthreshold: xsd_number = 8 Parameter(s) FabryPerotInterferometer is a Interferometer, (which) is a OpticalOperating Instrument (which) is a Mode Instrument Observatory hasfiltercentralwavelength: Wavelength haslowerboundformatioheight: Height Date/time ArticCircle is a GeographicRegion Return-type: data haslatitudeboundary: haslatitudeupperboundary: Date/time when KP => 8

7 Why we were led to semantics - When we integrate, we integrate concepts, terms In the past we would ask, guess, research a lot, or give up It s pretty much about meaning Semantics can really help find, access, integrate, use, explain, trust 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?

8 What is an Ontology? Thesauri narrower Catalog/ term ID relation Terms/ glossary Formal Frames General is-a (properties) Logical constraints Informal is-a Disjointness Formal Value, Inverse, instance Restrs. part-of *based on AAAI 99 Ontologies panel McGuinness, Welty, Ushold, Gruninger, Lehmann

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10 Instrument Class Excerpt OpticalInstrument Interferometer Fabry-PerotInterferometer MichelsonInterferometer IRMichelsonInterferometer DopplerMichelsonInterferometer AirglowImager AllSkyCamera Lidar Spectrometer Polarimeter Heliograph Photometer SingleChannelPhotometer MultiChannelPhotometer Taxonomy of instruments covering content areas. Currently expanding and evaluating. 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

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12 CEDAR ontology - parameters PhysicalQuantity referstorealm Density ElectronDensity NeutralDensity measuredby: mass, number Pressure NeutralPressure Temperature IonTemperature ElectronTemperature NeutralTemperature Field hasmagnitude: xsd::number FieldComponent hasdirection hascoordinatesystem MagneticFieldComponent ElectricFieldComponent VelocityFieldComponent Wavelength: hasinterval Wavenumber: hasinterval Index hassamplingrepresentation Geophysical Solar StatisticalMeasure Covariance ChiSquare ReducedChiSquare CrossCorrelation Coherence Curtosis StatisticalOperation (SWEET) numerics.owl

13 VSTO - semantics and ontologies in an operational environment: vsto.hao.ucar.edu, Web Service

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21 Developing ontologies Begin with use cases Small team (7-8; 2-3 domain experts, 2 knowledge experts, 1 software engineer, 1 facilitator, 1 scribe) Identify classes and properties Minimal properties to start, add only when needed Vet (within science communities, representation community, ) Only code them (in RDF or OWL) when needed Keep the ontologies small and modular Prototype the application Evaluate and iterate Lots of tools: CMAP, Protégé, SWOOP, white board (experiences, results, lessons learned, AND your ontologies AND discussions)

22 VSTO Remarks VSTO in production (Cedar, Mauna Loa - vsto.org ) Web services were easy to create Obtain unexpected and expected benefits Unified workflow around Instrument/Parameter/ Data-Time Next are working on event/phenomenon To date, the ontology creation and evolution process is working well (extensible, reusable, )

23 Technical Foundation Directions Beyond expanding science coverage. - working with other ontology-driven projects to create modular, reusable, community-authored ontologies - designing infrastructure to support evolution - Wiki-style site - Chimaera-style diagnostic and merging tools - reasoner supported validation tools - environmental support for explanation, collaborative development,

24 Discussion points Semantic Technologies are ready for use; not just by computer scientists Our infrastructure provides an operational prototype Our ontologies have proven themselves to be reusable and extensible Our development model of tight coupling between science and representation experts, small team, iterative development, broad vetting works well We welcome collaborators, partners, reviewers, More info: