Hydrologic Ontologies Framework (HOW)

1. Page 1 Hydrologic Ontologies Framework (HOW) Michael Piasecki, Bora Beran Department of Civil, Architectural, and Environmental Engineering Drexel University Luis Bermudez Monterrey Bay Aquarium Research Institute (MBARI) 3rd GEON Annual Meeting San Diego, CA May 5-6, 2005 Drexel University, College of Engineering

2. Page 2 Background Consortium of Universities for the Advancement of the Hydrologic Sciences, Inc. funded through EAR Hydrology Program (PD Doug James) Hydrologic Information Systems (HIS) Group: Rick Hooper (President CUAHSI) David Maidment (UT Austin) John Helly (SDSC) Praveen Kumar (UIUC) Michael Piasecki (Drexel U.) The objective of HIS is: • to develop a Hydrologic Information System prototype Community Metadata Profile Digital Library System Digital Watershed Drexel University, College of Engineering

3. Lets focus on this …………… Page 3 Why Hydrologic Ontologies? • To resolve semantic heterogeneities between disparate metadatadescriptions, e.g. “Gauge Height = Stage = Stream Gauge”, by representing metadata profiles in the Web Ontology Language. • To create a Hydrologic Controlled Vocabulary for navigation and discovery of hydrologic data, e.g. a framework that aids discovery(on a more generalized level) and defines markup (on a finer or “leaf” level) to identify specific data sets within a Digital Library. • To develop a conceptual representation for the Hydrologic Domainwithin which data discovery and information extraction can be inferredfrom knowledge representations. Drexel University, College of Engineering

4. GEON Upper Hydrologic Ontology Hydrologic ProcessesSedimentation Many More Many More Many More Many More ARCHydro ISO 19115 Geospatial ISO 19108 Temporal Objects ISO 19103 Units/Conversion USGS Hydrologic Unit Code Page 5 Status of work in CUAHSI We currently have What we need is OntologyExamples Drexel University, College of Engineering

5. Page 6 Example Use Drexel University, College of Engineering

6. Page 7 GEON sponsored Mini Workshop • San Diego Supercomputer Center January 27-28, 2005 Many thanks to Chaitan Baru (agree to sponsor) and Margaret Banton for organizing. • ParticipantsMichael Piasecki Drexel University (convener) • David Maidment University of Texas, Austin • Thanos Papanicolaou University of Iowa • Edwin Welles NOAA, National Weather Service, OHD • Luis Bermudez Monterrey Bay Aquarium Research Institute (MBARI) • llya Zaslavsky SDSC • Kai Lin SDSC • Ashraf Memon SDSC • Objective:Discuss concepts for Upper Hydrologic Ontology Drexel University, College of Engineering

7. Page 8 Be cognizant of ………. A few rules: 1) There is no one correct way to model a domain— there are always viable alternatives. The best solution almost always depends on the application that you have in mind and the extensions that you anticipate. 2) Ontology development is necessarily an iterative process. 3) Concepts in the ontology should be close to objects (physical or logical) and relationships in your domain of interest. These are most likely to be nouns (objects) or verbs (relationships) in sentences that describe your domain. Drexel University, College of Engineering

8. subclass:precip subclass:atmos water subclass:……… subclass:…….. 15 km ~2 m -1 km Page 9 1st Alternative Hydrologic Ontologies GeoVolume concepthorizontal slices no vertical tracing Pros: categorization along spatial separations, easy to follow closely linked to hierarchical structure of CV traditional linkage to disciplines and sub-disciplines horizontal flow path is well representedmodel domains are typically aligned with horizontal layers Cons: vertical flow (budget) not represented well need prior knowledge in which domain to search for dataprocesses are sub-items on low levels of ontology, this may not suit the general idea of moving from more general to more specific concepts class:hydrology subclass:surface water subclass:sub-surf. water Drexel University, College of Engineering

9. Page 10 2st Alternative Hydrologic Ontologies Measurement concepteverything is a measure expand to include phenomena & features Feature:Basin Pros: a very general concept that potentially serves all purposes could be linked with other domains possible use of only ONE upper ontology model Cons: processes, data models are not easily mapped or found no hierarchical navigation difficult when trying to use for CV or keyword lists might be difficult for “new” knowledge discovery Curve-# SCS => derived Phenomenon:Rainfall Intensity NEXRAD => derivedgauge => measured Substance:Water Temperatu pH Drexel University, College of Engineering

10. subclass:Sediment subclass:models subclass:Heat Flux subclass:Flooding …. dimension Type Page 11 3st Alternative Hydrologic Ontologies “Interests” concept models (prediction, analysis) data models (obs, measurements) processes (phenomena) representations (maps, time series, …) class:hydrology Pros: direct link to processes & data models of interest can link data sets directly with processes can make use of many already existing conceptualizations models (statistical, deterministic etc) can be well mapped Cons: not very good for hierarchical navigationthere is no general -> specific transitiondifficult when trying to use for CV or keyword lists might be difficult for “new” knowledge discovery subclass:data subclass:processes Data Model ArcHydro Drexel University, College of Engineering

11. Page 12 Outcomes Hydrologic Ontologies • Development of a Higher level Hydrologic ontology based on the afore mentioned concepts. The group felt no clear affinity for one or the other concepts. As a result, two or three top ontologies may need to be developed and placed next to each other. Depending on the taskat hand a user may use either one of them to address the objective. • Development of lower ontologies that can be merged with the top ontology. a) development of ontologies from database schema (like ARCHydro and the NWIS data base) via XML schema libraries b) development of a processes (or phenomena) ontology c) development of modeling ontology d) inclusion of task specific (service) ontologies, e.g. units, temporal • Development of a well defined Hydrologic Controlled Vocabulary that can be used to query the hydrologic realm. One suggestion made was to use common queries as a starting point to identify important aspects in the taxonomy of the CV. Drexel University, College of Engineering

12. Page 13 Application Hydrologic Ontologies Upper Ontology: Measurements coupled with Lower Ontology: HUC system HYDROOGLE Drexel University, College of Engineering

13. Page 14 Thank you Questions? Additional Information http://loki.cae.drexel.edu:8080/web/how/me/metadatacuahsi.html Drexel University, College of Engineering

14. Page 2 Pros: a very general concept that potentially serves all purposes could be linked with other domains possible use of only ONE upper ontology model Cons: processes, data models are not easily mapped or found no hierarchical navigation difficult when trying to use for CV or keyword lists might be difficult for “new” knowledge discovery Drexel University, College of Engineering