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Semantic Web for Water Data Interoperability

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Semantic Web for Water Data Interoperability

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Semantic Web for Water Data Interoperability

  1. 1. Semantic Web for Water Data Interoperability Boyan Brodaric Geological Survey of Canada
  2. 2. Water Data Networks Groundwater CAN GW Info Network (GIN) US Nat’l GW Monitoring Network Surface Water CUAHSI – US Universities GEOSS - International Standards-based Open geospatial standards Semantic Web standards 2 Context: Big Data
  3. 3. 3 Applications: Big Science  Global groundwater modeling  Global groundwater monitoring
  4. 4. 4 Problem: gw data heterogeneity  Ontario & Quebec syntactic, schematic, semantic heterogeneity in water-well data Quebec rock type Ontario rock type
  5. 5. 5 Problem: sw data heterogeneity  diverse measured parameters in CUAHSI many agencies, 1000’s of parameters Piasecki & Brean 2009
  6. 6. OGC Standards Metadata, Use Profile Feature Type Catalog 6 Solution: data interoperability Semantic Web Proof, Trust OWL ontology RDF triplet RDF, OWL, SPARQL WOA: URI, HTTP Interoperability Data usage Data content Data structure pragmatic syntax Data language Data systems semantic schema system GWML, WaterML XML, GML SOA: SOAP, HTTP
  7. 7. Data Interoperability: SDI architecture 7 NGWMN Portal GWML1 WaterML2 WMS WFS SOS Data translation Data integration Cache GML O&M WMS WFS SOS Data Portal data use GIN Portal Data Pipeline data transfer mediator Ontology Data data supply Catalog NRCan ON QC … USGS IILL … GWML, WaterML2, Excel, PDF, Ascii,… GWML, WaterML2
  8. 8. 8 Data interoperability: example schematic GIN simple lithology ontology Lithology GWML <lithology> … <name…>Sand</name> </lithoogy> syntactic semantic ON Sand QC Sand
  9. 9. 9 Data interoperability: gw features  CAN: water wells (8 provinces), key aquifers  USA: water wells (USGS, >20 states), nat’l aquifers
  10. 10. 10 Data interoperability: gw observations  CAN: groundwater level (3 provinces)  USA: groundwater level & quality (29 states)
  11. 11. 11 Semantic heterogeneity  Emerging water data standards
  12. 12. 12 Semantic heterogeneity  what’s a ‘groundwater body’ specific amount of matter or the object composed of the matter? - e.g. water body of the Ogallala aquifer or is a timeless object but its water matter (slowly) changes over time - water quality issue: the matter travels, object is fixed - water quantity issue: the matter disappears (dry aquifer), object persists fills a void? - water quantity and quality issue: size and connection of voids constrains quantity and flow contrast in int’l groundwater data standards: INSPIRE GWML object or matter? object no voids object fills voids
  13. 13. 13 Semantic heterogeneity  what’s a ‘surface water body’ - contains water, connected, navigable? contrast in European national water feature standards (Duce & Janowicz, 2010) : River (DE) River (SP) contains water possibly dry connected possibly not connected navigable possibly not navigable W h a t ’ s a w a t e r b o d y ?
  14. 14. Semantic interoperability: ontologies 14  reference ontology - canonical conceptual model for the domain - to disambiguate concepts e.g. for data standards design - heavy vs light analogous to reference manual vs user guide reference ontology is necessarily heavy (complete, formal, rigorous) Reference ontology
  15. 15. Semantic interoperability: ontologies 15  reference ontology: non-contextual Foundational (general) Domain (essential) Application (contextual) (after Guarino, 1998) matter constitutes objects water matter constitutes a water body H2O + various ingredients potable water constitutes stored w body specific chemical content physical object constituted by matter water body can be constituted by water can be connected can have human uses (Duce & Janowicz, 2010) Spanish River can be dry (no water) may not connect not navigable German River has water connected navigable
  16. 16. Semantic interoperability: ontologies 16 Reference Ontology Upper-Level ontology (DOLCE ‘amount-of-matter’) Application ontology (QC ‘matprim’, QC ‘SABL’) Application ontology (ON ‘material1’, ON ‘sand’) SABL ARGL TERR sand clay soil Domain ontology (GIN-GeoSciML ‘lithology’, GIN-GeoSciML ‘sand’) general concepts public schema public vocabulary local schema local vocabulary
  17. 17. Elements of a reference hydro ontology  contrast concepts: different natural situations for gw & sw  boundary concepts: bridge between gw & sw, e.g. baseflow  common concepts: shared container concepts for gw & sw 17 Lake / River
  18. 18. 18 Essential common concepts  container schema for water water body http://myloupe.com/home/info-price- rm.php?image_id=161322# flow container container matter water matter void
  19. 19. 19 Essential common concepts  container schema for water water flow container object container matter void water body object water matter Surface water body Subsurface water body
  20. 20. 20 Essential common concepts  hydro-ontologic square - entities: physical body, void, matter, water body - relations: hosting-a-void, containment, constitution physical body contains FOIS 2012 FOIS 2012 constituted-by (earth material) contains water body matter hosts void hosts contains constituted-by (water material) COSIT 2013 COSIT 2013 FOIS 2014 FOIS 2014
  21. 21. 21 Constitution  …why a water body is like a statue - object persists if matter is replaced e.g. statue of liberty and torch matter e.g. river and a plume (Hahmann & Brodaric, 2014)  … or not - object can persist if matter is absent e.g. dry river (Rio Grande segments) - object can persist if shape changes   water body  matter  container - water body persists when matter is replaced - container persists when water body ceases - numerically distinct wb
  22. 22. 22 has quality has quality perdurant endurant physical object amount of matter feature constitutio n hosting process volume water flow rock matter water matter ground void depression water body rock body quality participation containment river aquifer hole gap river DE river SP gw body GWML gw body INSPIRE ? Application Domain Foundational Tiered hydro ontology
  23. 23. 23 E-science  reference ontology - not only for interoperability of ‘big data’ - also for representing theories and hypotheses, to aid discovery Theory  hypothesis modelling application ontologies theorizing STORM SEVERITY (S) = 4.943709 + (-.000777 x CAPE) + (-.004005 x MWND) + (+.181217 x EHI) + (-.026867 x SPD) + (-.006479 x s-rH) (Nat’l Weather Service) Data Trends  law  empirical regularity data mining Observation  data Model sensing  prediction variables theories ontologies data interop
  24. 24. 24 Final thoughts  Operational deployment of massive water data networks is feasible  Interoperability of such networks is reliant on global standards: systems, syntax, schema, semantics, pragmatics  Progress on reference hydro ontology helps disambiguate conceptual differences informs data standards design provides a foundation for theoretical knowledge
  25. 25. 25 Thank you – Merci http://gw-info.net
  26. 26. 26 Role of ontology: hydro-informatics Hydrology Ontology  Modeling physical math numerical computing data how fast does river X flow? what are its water levels?  Reasoning conceptual philosophy / logic artificial intelligence propositions what is a river? is river X navigable?

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