Semantic Web for Water Data Interoperability

1. Semantic Web for Water Data Interoperability
Boyan Brodaric
Geological Survey of Canada

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
Applications: Big Science
 Global groundwater modeling
 Global groundwater monitoring

4. 4
Problem: gw data heterogeneity
 Ontario & Quebec
syntactic, schematic, semantic
heterogeneity in water-well data
Quebec rock type
Ontario rock type

5. 5
Problem: sw data heterogeneity
 diverse measured parameters in CUAHSI
many agencies, 1000’s of parameters
Piasecki & Brean 2009

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. 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
Data interoperability: example
schematic
GIN simple lithology ontology
Lithology GWML
<lithology>
…
<name…>Sand</name>
</lithoogy>
syntactic
semantic
ON Sand
QC Sand

9. 9
Data interoperability: gw features
 CAN: water wells (8 provinces), key aquifers
 USA: water wells (USGS, >20 states), nat’l aquifers

10. 10
Data interoperability: gw
observations
 CAN: groundwater level (3 provinces)
 USA: groundwater level & quality (29 states)

11. 11
Semantic heterogeneity
 Emerging water data standards

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
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. 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. 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. 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. 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
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
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
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
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
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
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
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
Thank you – Merci
http://gw-info.net

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?