Semantic Web for Water Data Interoperabil i ty

Boyan Brodaric Geological Survey of Canada

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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

Context: Big Data

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Applications: Big Science

Global groundwater modeling

Global groundwater monitoring

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Problem: gw data heterogeneity

Ontario & Quebec syntactic, schematic, semantic

heterogeneity in water-well data

Quebec rock type

Ontario rock type

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diverse measured parameters in CUAHSI many agencies, 1000’s of parameters

Piasecki & Brean 2009

Problem: sw data heterogeneity

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Solution: data interoperability

WOA: URI, HTTP

RDF, OWL, SPARQL

RDF triplet

OWL ontology

Proof, Trust

Semantic Web

Data systems

Data content

Data structure

Data usage

schema

semantic

system

syntax Data language

pragmatic

Interoperability

SOA: SOAP, HTTP

XML, GML

GWML, WaterML

Feature Type Catalog

Metadata, Use Profile

OGC Standards

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Data Interoperability: SDI architecture

WMS WFS SOS

WMS WFS SOS

GWML1 WaterML2

Data Portal data use

GIN Portal

Data Pipelinedata transfer

Datadata supply

NRCan QC … USGS ILIL …ON

GML O&M

Data translation

Data integration Cache

Catalog

NGWMN Portal

mediatorOntology

GWML, WaterML2

GWML, WaterML2, Excel, PDF, Ascii,…

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schematic

GIN simple lithology ontology

Lithology GWML<lithology> … <name…>Sand</name></lithoogy>

syntactic

semantic

ON Sand

QC Sand

Data interoperability: example

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Data interoperability: gw features

CAN: water wells (8 provinces), key aquifers

USA: water wells (USGS, >20 states), nat’l aquifers

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Data interoperability: gw observations

CAN: groundwater level (3 provinces)

USA: groundwater level & quality (29 states)

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Emerging water data standards

Semantic heterogeneity

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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

object or matter?

no voids

GWML

object

object fills voids

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Semantic heterogeneity

what’s a ‘surface water body’

- contains water, connected, navigable?

contrast in European national water feature standards (Duce & Janowicz, 2010) :

River (DE)

contains water

connected

navigable

River (SP)

possibly dry

possibly not connected

possibly not navigable

What’s a

water

body?

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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

Semantic interoperability: ontologies

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reference ontology: non-contextual

Foundational (general)

Domain (essential)

Application (contextual)

(after Guarino, 1998)

matterconstitutes objects

water matterconstitutes a water bodyH2O + various ingredients

potable waterconstitutes stored w bodyspecific chemical content

physical objectconstituted by matter

water bodycan be constituted by watercan be connected can have human uses

Spanish Rivercan be dry (no water)may not connectnot navigable

German Riverhas waterconnected navigable

(Duce & Janowicz, 2010)

Semantic interoperability: ontologies

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Reference Ontology

Application ontology

(QC ‘matprim’, QC ‘SABL’)

Application ontology

(ON ‘material1’, ON ‘sand’)

SABL

ARGL

TERR

sand

clay

soil

Upper-Level ontology

(DOLCE ‘amount-of-matter’)

Domain ontology

(GIN-GeoSciML ‘lithology’,

GIN-GeoSciML ‘sand’)

local schemalocal vocabulary

public schemapublic vocabulary

general concepts

Semantic interoperability: ontologies

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Elements of a reference hydro ontology

Lake / River

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

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http://myloupe.com/home/info-price-rm.php?image_id=161322#

container

flow

container matter

water matter

water body

void

Essential common concepts

container schema for water

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container object

container matter

water body object

water matter

water flow

void

Surface water body

Subsurface water body

Essential common concepts

container schema for water

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Essential common concepts

physical body

water body

matter

voidhosts

hosts

constituted-by(water material)

constituted-by(earth material) contains

hydro-ontologic square- entities: physical body, void, matter, water body

- relations: hosting-a-void, containment, constitution

contains

contains

FOIS 2012

FOIS 2012

COSIT 2013

COSIT 2013

FOIS 2014

FOIS 2014FOIS 2014

FOIS 2014

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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

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physical object

amount of matter

featureconstitutio

n

hosting

process

volume

water flow

rock matter

water matter

voidground depression

water body

rock body

perdurant endurant

quality

participation

has quality has qualitycontainment

gapholeriver aquifer

river DE river SPgw body

GWML

gw bodyINSPIRE ?

Ap

plica

tio

n D

om

ain

F

ou

nd

ati

on

al

Tiered hydro ontology

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E-science

reference ontology- not only for interoperability of ‘big data’

- also for representing theories and hypotheses, to aid discovery

Theory hypothesis

application

theorizingSTORM 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

Observation data

data mining

Model predictionsensing

modelling ontologiesvariablestheories

ontologiesdata interop

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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

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Thank you – Merci

http://gw-info.net

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Role of ontology: hydro-informatics

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?

OntologyHydrology