1. Ontologies for Earth System Science: Experiences in the Development of a Community Knowledge Base Rob Raskin NASA/Jet Propulsion Laboratory Pasadena, CA Feb 22, 2008

2. Data to Knowledge Data InformationKnowledge Basic ElementsBytes NumbersModelsFacts ServicesSave Visualize Infer Understand Predict StorageFile Database GISOntology Mind VolumeHighLow DensityLowHigh SyntaxSemantics

3. What is Knowledge? Facts, relations, meanings, contexts, common sense Information with context Shared understanding of meaning Suitable for reasoning/inference Dynamic, expandable

4. Application: Intelligent Search for Data Consults knowledge base to find alternative meanings Clustered by: synonyms, parent, children Enables discovery of resources without exact keyword match Semantic understanding is crucial Common search engines (Google) use these capabilities only minimally, at present

5. Application: Intelligent Search for Data (more) Noesis ontology-aided search tool http://noesis.itsc.uah.edu Provides access to: Data Journal articles Web pages Experts (people)

6. Semantic Understanding is Difficult! Variable t: temperature Variable t: time Sea surface temperature: measured 3 m above surface Sea surface temperature: measured at surface Data quality= 5 Data quality= 3

7. Let’s eat, Grandma. Let’s eat Grandma. Time flies like an arrow. Fruit flies like a pie. Semantic Understanding is Difficult! (more) LA Times headline “Mission accomplished. Major combat operations in Iraq have ended” -Pres. Bush, 2003 “not a nobody” “ain’t nobody” “yeah, right”

8. Knowledge Base of Facts as Triples Subject-Verb-Object representation Flood is a WeatherPhenomena GeoTIFFis aFileFormat Soil Typeis aPhysicalProperty Pacific Oceanis a Ocean Ocean has substanceWater SensormeasuresTemperature

9. Semantic Web Vision Web page creators place XML tags around technical terms on web pages XML tags point to knowledge base (namespace) where term is “defined” This vision has not caught on, in part due to the success of free-text searching without semantic aid

10. Ontologies Method to store “facts” General definition: “all that is known” Computer science definition: Machine-readable definition of terms and how they relate to one another As with a dictionary, terms are defined in terms of other terms Provide shared understanding of concepts Support knowledge reuse Support machine-to-machine communications with deeper semantics than controlled vocabulary

11. Ontology Properties Standard languages make it easy to extend (specialize) concepts developed by others Synonym support (multiple terms with same meaning) Label available to indicate preferred term for each community Homonym support (multiple meanings of same term) Separate namespaces ( President:Bush vs Plant:Bush)

12. Plate tectonics - before Plate Tectonics Ontology

13. Atmosphere Ontology… Atmosphere Ontology

14. Ontology Languages: RDF and OWL W3C has adopted XML-based standard ontology languages Resource Description Formulation (RDF) Ontology Web Language (OWL) Languages predefine specific tags RDF: Class, subclass, property, subproperty, … OWL: Extends RDF to predefine further tags such as cardinality Three flavors of OWL (Lite, DL, and Full) Use of standard languages make it easy to extend (specialize) work of others

15. Semantic Web for Earth and Environmental Terminology (SWEET) Concept space written in OWL Initial focus to assist search for data resources Funded by NASA Later focus to serve as community standard Enables scalable classification of Earth system science concepts

16. Non-Living Substances Living Substances Physical Processes Earth Realm Physical Properties Time Natural Phenomena Human Activities Integrative Ontologies Space Data Faceted Ontologies Units Numerics SWEET 1.0 Ontologies

17. SWEET 2.0 Ontologies: Modular Design MathematicsUnits Electric/ Magnetism TimeSpace Radiation Transfer Geophys Fluid Dynam Human Activities Climate Change Troposphere Thermo Land Surface Waves HeliosphereCryosphere Mechanics Basic Science/ Math Supporting Geophysical Phenomena Planetary Realms Applications Air Pollution Water Resources Geosphere Ecosphere Biogeo- chemistry Planetary Structures import Ocean Upper Atmosphere Geo- magnetism Planetary Gravity Biogeochem Cycles Energy etc. Chemistry

18. SWEET as an Upper Level Earth Science Ontology MathPhysicsChemistry Space Time Property EarthRealm Process, Phenomena Substance Data Stratospheric Chemistry Biogeochemistry Specialized domains import SWEET

19. SWEET Numerical Ontologies Intervals, numeric relations ( ) Cartesian products Functions, derivatives Fuzzy concepts “near” Spatial concepts 0-D, 1-D, 2-D, and 3-D objects Coordinate systems Above, inside, etc. Temporal concepts Instant, durations, geological time scales

20. SWEET Data Ontology Dataset characteristics Format, data model, dimensions, … Provenance Source, processing history, … Parameters Scale factors, offsets, … Data services Subsetting, reprojection, … Quality measures Special values Missing, land, sea, ice,...

21. Web Services Ontology OWL-S Enables semantic descriptions of Web service inputs and outputs Provides binding to actual implementation of Web service

22. Best Practices Keep ontologies small, modular Be careful that “Owl:Import” imports everything Use higher level ontologies where possible Identify hierarchy of concept spaces Model schemas Try to keep dependencies unidirectional Gain community buy-in Involve respected leaders

23. PlanetOnt.org

24. Technology  Geospatial semantic services established  Geospatial semantic services proliferate  Scientific semantic assisted services  SWEET 3.0 with semantic callable interfaces via standard programming languages  SWEET core 2.0 based on best practices decided from community  Scientific reasoning  Reasoners able to utilize SWEET 4.0  Local processing + data exchange  Basic data tailoring services (data as service), verification/ validation  Interoperable geospatial services (analysis as service), explanation  Common vocabulary based product search and access Interoperable Information Infrastructure Assisted Discovery & Mediation  Metadata-driven data fusion (semantic service chaining), trust  Semantic agent-based integration  Semantic agent-based searches  Geospatial reasoning, OWL- Time Capability Results  Improved Information Sharing  Increased Collaboration & Interdisciplinary Science  Acceleration of Knowledge Production  Revolutionizing how science is done  RDF, OWL, OWL-S Semantic Web Roadmap Current Near Term Mid Term Long Term  Autonomous inference of science results  Numerical reasoning  Semantic geospatial search & inference, access  SWEET core 1.0 based on GCMD/CF Vocabulary Languages / Reasoning Output Outcome

25. Ontology Development Tools: CMAP Free, downloadable tool for knowledge representation and ontology development Visual language with input/export to OWL Supports subset of OWL language http://cmap.ihmc.us/coe

26. Other Ontology Editors Public Domain SWOOP Mindswap Lab (UMBC) http://www.mindswap.org/2004/SWOOP Protégé Stanford http://protege.stanford.edu Commercial Cerebra Construct http://cerebra.com TopBraid Composer http://www.topquadrant.com/topbraid/composer

27. Resources SWEET http://sweet.jpl.nasa.gov Ontology development/sharing site http://PlanetOnt.org Noesis (search tool) http://noesis.itsc.uah.edu SESDI http://sesdi.hao.ucar.edu