< MMI />

1. <MMI /> Why are Ontologies Important ? Luis Bermudez QARTOD III November 2-4, 2005

2. Ontology-Philosophy “ Most fundamental branch of metaphysics. It studies being or existence as well as the basic categories thereof—trying to find out what entities and what types of entities exist. ” - Wikipedia

4. “ Because any ontology is, among other things, a social / cultural artifact, there is no purely objective perspective from which to observe the whole terrain of concepts. Instead of asking, “what hierarchical representation of concepts best captures the universal relationships among general ideas,” it is more productive to ask “what specificpurpose do we have in mind for this conceptual map of entities and what practical difference will this ontology make? ” -Wittgenstein, Tractatus Logico-Philosophicus.

6. Earth Realms Physical Phenomena Physical Processes Physical Properties Physical Substances Sun Realms Biosphere Data Data Centers Human Activities Material Things Numerics Sensors Space Time Units SWEET Ontologieshttp://sweet.jpl.nasa.gov/ontology/

7. Ontologies - Computer ScienceSpecification of conceptualizations Lake River Example: 1. Properties of real world objects are identified. 2. Similarities are identified. 3. Concepts are created 4. and are expressed as a class. 5. Classes are related. Is inland body Has a relative defined channel Has water Body of Water Class Subclass Lake River

8. Class Looks like a Real world objects Is Transitive Is part of Region Subregion Accounting Unit Cataloging Unit Is part of Is part of Is part of Infer isPartOf Hydrologic Unit Mid Atlantic Delaware Lower Delaware Subclasses Schuylkill Instances

9. What is an Ontology? Thesauri “narrower term” relation Frames (properties) Formal is-a General Logical constraints Catalog/ ID Informal is-a Formal instance Disjointness, Inverse, part-of… Terms/ glossary Value Restrs. Deborah McGuinness

10. Why Ontologies • To share common understanding of the structure of information among people or software agents • To enable reuse of domain knowledge • To make domain assumptions explicit • To separate domain knowledge from the operational knowledge • To analyze domain knowledge Cartic Ramakrishnan LSDIS Lab, University of Georgia

11. Why do we have a presentation about ontologies in a QARTOD meeting ?

12. Do we need to share explictly QARTOD concepts ? • Quality Levels • Flags • Sensors • Instrument Methodology • Calibration procedures • QC software procedures • Methods of verification and validation • Methods for manual checking • Malfunctions

13. Can we state this explictly ? WHP CTD data quality codes WMO IGOSS observation quality codes

14. MMI and ontologies

15. BODC TEMP Semantic Issues Search for seatemperature data GCMD GCMD Sea surface Temperature Ocean Temperature CF sea_water_ temperature Don’t sure what data will get retrieved ?

16. Solving semantic issues

17. Harmonization HTML Comma Separated Values Tab Separated Values DTD OWL XML/XSD Relational Database RDF

18. Web Ontology Language: OWL • W3C Recommendation 02/04. • Based on RDF. (-> URI ) • Inference capabilities. • Restriction of inherit properties. • Can be used to express specifications and vocabularies Body of Water River <owl:Class rdf:ID=“Body_of_Water”></owl:Class> <owl:Classrdf:ID=“River”> <rdfs:subClassOf rdf:resource=“# Body_of_Water”/> </owl:Class>

19. Domain Ontologies Repository

20. VOC2OWL

21. VINE • Vocabulary Integration Environment

22. Community role

23. Mapping Results

25. http://marinemetadata.org

26. Ingests from instruments Data Source processes: formats/archives sends gets Community gets Guides & Tools sends Technology processes: formats/archives processes: uses/analyzes gets publishes Data User Data Provider

27. About MMI • MMI = Marine Metadata Interoperability Initiative. • NSF funded and SURA (Southeastern Universities Research Association) supported. • Initially one year project (September 2005). In the process of getting extended (NOAA and NSF). • Organization; Executive committee (5), Steering committee (17), technical committee(~25), and contributors. Community of more than 200 members (October 2005). • Deliverables: • Community web site with metadata content, guidance • Interoperability Demonstrations • Workshop: “Advancing Domain Vocabularies” • Tools : VINE Voc2OWL, Tethys, Web Services

28. http://marinemetadata.org/tethys • Implement two methods and make them available using SOAP web services. • Convert the parameters, sources, and units used in their system to an ontology. (tool VOC2OWL ascii to OWL) • Map the terms used in the system to the MMI preferred ontology: Standard vocabulary for discovery (GCMD) and for usage (CF).

29. Steering Committee Members • John Graybeal, MBARI. PI. (ExecComm) graybeal@mbari.org • Stephanie Watson, CeNCOOS. (ExecComm) swatson@mbari.org • Philip Bogden, SURA/SCOOP. (ExecComm) bogden@gomoos.org • Stephen Miller, Scripps. (ExecComm) spmiller@ucsd.edu • Robert Arko, LDEO • Julie Bosch, NOAA • Francisco Chavez, MBARI • Ben Domenico, Unidata • Karen Stocks, SDSC • Steve Hankin, NOAA - Ocean.US/DMAC • Roy Lowry, BODC • Mark Musen, Stanford Univ • Michael Parke, Univ of Hawaii • Lola Olsen, NASA Goddard • Dawn Wright, Oregon State Univ • Bob Weller, WHOI

30. Credits • National Science Foundation1 • SURA, the Southeastern Universities Research Association (http://www.sura.org), • NOAA (including the Coastal Services Center), • ONR, the Office of Naval Research (http://www.onr.navy.mil), • OceanUS and regional IOOS systems. 1 NSF Grant ATM-0447031

31. Thank you !bermudez@mbari.orgask@marinemetadata.org