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Ontologies and Semantic Applications in Earth Sciences

Ontologies and Semantic Applications in Earth Sciences. Peter Fox (TWC/RPI; formerly HAO/NCAR) Thanks to many. Projects funded by NSF/OCI and NASA/ACCESS/ESTO. Background. Scientists should be able to access a global, distributed knowledge base of scientific data that:

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Ontologies and Semantic Applications in Earth Sciences

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  1. Ontologies and Semantic Applications in Earth Sciences Peter Fox (TWC/RPI; formerly HAO/NCAR) Thanks to many. Projects funded by NSF/OCI and NASA/ACCESS/ESTO 20081118 Fox OOS meeting

  2. 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 means (models and 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 And… there exist(ed) significant levels of semantic heterogeneity, large-scale data, complex data types, legacy systems, inflexible and unsustainable implementation technology

  3. Lightweight semantics Limited meaning, hard coded Limited extensibility Under review Data-types as service Limited interoperability • VOTable • Simple Image Access Protocol • Simple Spectrum Access Protocol • Simple Time Access Protocol VO App2 VO App3 VO App1 Open Geospatial Consortium: Web {Feature, Coverage, Mapping} Service Sensor Web Enablement: Sensor {Observation, Planning, Analysis} Service use the same approach VO layer DBn DB2 DB3 … … … … DB1

  4. Added value Education, clearinghouses, other services, disciplines, etc. Semantic interoperability Added value Added value Semantic query, hypothesis and inference Semantic mediation layer - mid-upper-level Added value VO API Web Serv. VO Portal “Knowledge” as service! Query, access and use of data Mediation Layer • Ontology - capturing concepts of Parameters, Instruments, Date/Time, Data Product (and associated classes, properties) and Service Classes • Maps queries to underlying data • Generates access requests for metadata, data • Allows queries, reasoning, analysis, new hypothesis generation, testing, explanation, etc. Semantic mediation layer - VSTO - low level Standard, or not, vocabularies and schema Metadata, schema, data DBn DB2 DB3 … … … … DB1 20080602 Fox VSTO et al.

  5. Semantic Web Methodology and Technology Development Process • Establish and improve a well-defined methodology vision for Semantic Technology based application development • Leverage any existing vocabularies Adopt Technology Approach Leverage Technology Infrastructure Science/Expert Review & Iteration Rapid Prototype Open World: Evolve, Iterate, Redesign, Redeploy Use Tools Analysis Use Case Develop model/ ontology Small Team, mixed skills 20080602 Fox VSTO et al.

  6. E.g. Science and technical use cases 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. • Extract information from the use-case - encode knowledge • Translate this into a complete query for data - inference and integration of data from instruments, indices and models Provide semantically-enabled, smart data query services via a SOAP web for the Virtual Ionosphere-Thermosphere-Mesosphere Observatory that retrieve data, filtered by constraints on Instrument, Date-Time, and Parameter in any order and with constraints included in any combination. 20080602 Fox VSTO et al.

  7. Web Service Existing OPeNDAP Service VSTO - semantics and ontologies in an operational environment: vsto.hao.ucar.edu, www.vsto.org 20080602 Fox VSTO et al.

  8. Semantic Web Services 20080602 Fox VSTO et al.

  9. Semantic Web Services OWL document returned using VSTO ontology - can be used both syntactically or semantically 20080602 Fox VSTO et al.

  10. Semantic Web Benefits • Unified/ abstracted query workflow: Parameters, Instruments, Date-Time across widely different disciplines • Decreased input requirements for query: in one case reducing the number of selections from eight to three • Semantic query support: by using background ontologies and a reasoner, our application has the opportunity to only expose coherent queries (portal and services) • Semantic integration: in the past users had to remember (and maintain codes) to account for numerous different ways to combine and plot the data whereas now semantic mediation provides the level of sensible data integration required, and exposed as smart web services • understanding of coordinate systems, relationships, data synthesis, transformations, etc. • returns independent variables and related parameters • A broader range of potential users (PhD scientists, students, professional research associates and those from outside the fields) • VSTO: http://vsto.hao.ucar.edu, http://www.vsto.org

  11. http://dataportal.ucar.edu/schemas/vsto_all.owl (1.0, 2.0 coming) Fox RPI: Semantic Data Frameworks May 14, 2008

  12. Ingest/pipelines: problem definition • Data is coming in faster, in greater volumes and outstripping our ability to perform adequate quality control • Data is being used in new ways and we frequently do not have sufficient information on what happened to the data along the processing stages to determine if it is suitable for a use we did not envision • We often fail to capture, represent and propagate manually generated information that need to go with the data flows • Each time we develop a new instrument, we develop a new data ingest procedure and collect different metadata and organize it differently. It is then hard to use with previous projects • The task of event determination and feature classification is onerous and we don't do it until after we get the data

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  14. Use cases • Who (person or program) added the comments to the science data file for the best vignetted, rectangular polarization brightness image from January, 26, 2005 1849:09UT taken by the ACOS Mark IV polarimeter? • What was the cloud cover and atmospheric seeing conditions during the local morning of January 26, 2005 at MLSO? • Find all good images on March 21, 2008. • Why are the quick look images from March 21, 2008, 1900UT missing? • Why does this image look bad?

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  17. Provenance • Origin or source from which something comes, intention for use, who/what generated for, manner of manufacture, history of subsequent owners, sense of place and time of manufacture, production or discovery, documented in detail sufficient to allow reproducibility • Knowledge provenance; enrich with ontologies and ontology-aware tools

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  19. Quick look browse 20080602 Fox VSTO et al.

  20. Visual browse

  21. Search and structured query Structured Query Search

  22. Search 20080602 Fox VSTO et al.

  23. Data Integration Use Case • Determine the statistical signatures of both volcanic and solar forcings on the height of the tropopause

  24. Detection and attribution relations…

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  26. SWEET 2.0

  27. Semantic framework indicating how volcano and atmospheric parameters and databases can immediately be plugged in to the semantic data framework to enable data integration.

  28. Faceted Search 20080602 Fox VSTO et al.

  29. Summary • Level of ontology encoding relates to use, e.g. • VSTO: • SPCDIS: • SESDI: Data integration needs higher level of curation of ontologies and mapping to data • Languages and tools • Rapid prototyping (PHP, Semantic MediaWiki) • Clean and simple (RDFS, Perl and SPARQL) • Complex and rich (Java, Protégé, Jena, Pellet, ELMO, Maven, Eclipse)

  30. Modified GEON Solution Framework Data Discovery Data Integration Level 1: Data Registration at the Discovery Level, e.g. Volcano location and activity Level 2: Data Registration at the Inventory Level, e.g. list of datasets by, types, times, products Level 3: Data Registration at the Item Detail Level, e.g. access to individual quantities Earth Sciences Virtual Database A Data Warehouse where Schema heterogeneity problem is Solved; schema based integration Ontology based Data Integration 20080602 Fox VSTO et al. A.K.Sinha, Virginia Tech, 2006

  31. Spare material 20080602 Fox VSTO et al.

  32. Example 1: Registration of Volcanic Data • Location Codes: • U - Above the 180° turn at Holei Pali (upper Chain of Craters Road) • L - Below Holei Pali (lower Chain of Craters Road) • UL - Individual traverses were made both above and below the 180° turn at Holei Pali • H - Highway 11 SO2 Emission from Kilauea east rift zone - vehicle-based (Source: HVO) Abreviations: t/d=metric tonne (1000 kg)/day, SD=standard deviation, WS=wind speed, WD=wind direction east of true north, N=number of traverses 20080602 Fox VSTO et al.

  33. Registering Volcanic Data (2) • No explicit lat/long data • Volcano identified by name • Volcano ontology framework will link name to location 20080602 Fox VSTO et al.

  34. Registering Atmospheric Data (2) 20080602 Fox VSTO et al.

  35. Building blocks • Data formats and metadata: IAU standard FITS, with SoHO keyword convention, JPeG, GIF • Ontologies: OWL-DL and RDF • The proof markup language (PML) provides an interlingua for capturing the information agents need to understand results and to justify why they should believe the results. • The Inference Web toolkit provides a suite of tools for manipulating, presenting, summarizing, analyzing, and searching PML in efforts to provide a set of tools that will let end users understand information and its derivation, thereby facilitating trust in and reuse of information. • Capturing semantics of data quality, event, and feature detection within a suitable community ontology packages (SWEET, VSTO)

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