A standard information transfer model scopes the ontologies required for observations Simon Cox, Laurent Lefort TDWG, Fremantle,

CSIRO Cox/TDWG 2008 Science relies on observations Provides evidence & validation Involves sampling A domain-independent terminology and information-model Supports data discovery and integration across discipline boundaries Scopes ontology developments

CSIRO Cox/TDWG 2008 What is “an Observation” Observation act involves a procedure applied at a specific time The result of an observation is an estimate of some property The observation domain is a feature of interest at some time After Fowler & Odell ca. 1997

CSIRO Cox/TDWG 2008 Examples The 7th banana weighed 270gm on the kitchen scales this morning The attitude of the foliation at outcrop 321 of the Leederville Formation was 63/085, measured using a Brunton on Specimen H69 was identified on by Amy Bachrach as Eucalyptus Caesia The image of Camp Iota was obtained by Aster in 2003 Sample WMC997t collected at Empire Dam on was found to have 5.6 g/T Au as measured by ICPMS at ABC Labs on The X-Z Geobarometer determined that the ore-body was at depth 3.5 km at 1.75 Ga The GCM simulation run today using CMIP3 indicated that the pressure field in the atmosphere tomorrow will be as given in pf999_ _1

CSIRO Cox/TDWG 2008 In “pictures”

CSIRO Cox/TDWG 2008 Generic pattern for observation data An Observation is an action whose result is an estimate of the value of some property of the feature-of-interest, obtained using a specified procedure Where’s the “observation location”? In the feature-of-interest - this reconciles remote, lab, and in-situ observations Conformant with ISO CSL and meta-model

CSIRO Cox/TDWG 2008 O&M vs. OBOE An Observation is an action whose result is an estimate of the value of some property of the feature-of-interest, obtained using a specified procedure NCEAS OBOE: An Observation is the Measurement of the Value of a Characteristic of some Entity in a particular Context

CSIRO Cox/TDWG 2008 TDWG examples

CSIRO Cox/TDWG 2008 Sampling strategies and relationships Domain feature type Observation SamplingFeature +samplingTime [0..1] +parameter [0..*] AnyFeature relatedObservation 0..* Intention sampledFeature SamplingPoint Specimen +materialClass +samplingMethod [0..1] +samplingLocation [0..1] +size [0..1] +currentLocation [0..1] SpatiallyExtensiveSamplingFeature SamplingCurve +length [0..1] SamplingSurface +area [0..1] SamplingSolid +volume [0..1] Station Section MapHorizon Plot Mine Traverse Borehole Traverse 0..* Complex relatedSamplingFeature 0..*

CSIRO Cox/TDWG 2008 Specimens and “outcrops”

CSIRO Cox/TDWG 2008 What’s this got to do with Ontologies? UML is a formal language UML vs. OWL … similarly expressive Especially if UML profile and «stereotype» used

Ontologies for observations Obrst Ontology Spectrum: One View weak semantics strong semantics Is Disjoint Subclass of with transitivity property Modal Logic Logical Theory Thesaurus Has Narrower Meaning Than Taxonomy Is Sub-Classification of Conceptual Model Is Subclass of DB Schemas, XML Schema UML First Order Logic Relational Model, XML ER Extended ER Description Logic DAML+OIL, OWL RDF/S XTM Syntactic Interoperability Structural Interoperability Semantic Interoperability From less to more expressive

Ontologies for observations Obrst Ontology Spectrum: One View weak semantics strong semantics Is Disjoint Subclass of with transitivity property Modal Logic Logical Theory Thesaurus Has Narrower Meaning Than Taxonomy Is Sub-Classification of Conceptual Model Is Subclass of DB Schemas, XML Schema UML First Order Logic Relational Model, XML ER Extended ER Description Logic DAML+OIL, OWL RDF/S XTM Syntactic Interoperability Structural Interoperability Semantic Interoperability From less to more expressive Problem: Very General Semantic Expressivity: Very High Problem: Local Semantic Expressivity: Low Problem: General Semantic Expressivity: Medium Problem: General Semantic Expressivity: High

CSIRO Cox/TDWG 2008 What’s this got to do with Ontologies? UML is a formal language UML vs. OWL … similarly expressive Especially if UML profile and «stereotype» used ISO profile models may be transformed into OWL without too much difficulty ISO will define a UML→OWL rule … but converting the O&M model just gives you an OWL representation of the schema Is this useful for reasoning? O&M model scopes the (discipline specific) ontologies required for observational data and describes the relationships between them

CSIRO Cox/TDWG 2008 Discipline or community profile feature of interest Types define a domain-model (e.g. Plot, Ecosystem, OrganismOccurence) observed property Belongs to the type of the feature-of-interest (e.g. organism count, taxon, time, location) procedure Standard procedures, suitable for the property-type result Standard scales suitable for the property-type (e.g. taxonomy)

CSIRO Cox/TDWG 2008 Ontology enabled profiles Step one: align ontology and O&M skeleton Step two: round trip transformation Transform UML model into OWL (done) Use OWL to develop vocabularies on top of O&M skeleton Use extended UML-based MDA process to generate XML schemas Motivations Better quality vocabularies Greater consistency of the conceptual model

CSIRO Cox/TDWG 2008 Vocabularies dependencies in O&M

CSIRO Cox/TDWG 2008 Wrap-up

CSIRO Cox/TDWG 2008 Development and validation of “O&M” Developed in the context of Geochemistry/Assay data OGC Sensor Web Enablement – environmental and remote sensing Subsequently applied in Water resources/water quality Oceans & Atmospheres Natural resources Taxonomic data Geology field data

CSIRO Cox/TDWG 2008 Scopes the ontologies for domain observations Feature types (feature of interest, sampling features) Observed properties Observation procedures, instruments, algorithms Scales, taxonomies

CSIRO Cox/TDWG 2008 O&M Status OGC Standard 2007 ISO – upcoming Key aspect of GeoSciML Basis for WaterML v2 Basis for Climate Science ML

CSIRO Cox/TDWG 2008 Motivation for developing a common model Cross-domain data discovery and fusion Re-usable service interfaces

Thank you Exploration & Mining Simon Cox Research Scientist Phone: Web: Contact Us Phone: or Web:

CSIRO Cox/TDWG 2008 Generations of “standards” & integration complexity ASCII-based DB-based Registries XML Model-driven generation of XML schemas Custom XSL transfo. & web services Distributed systems with same db schema UML & XML schemas Reuseable XML schema stack Master Data Manag t OWL ontologies Semantic integration EPA STORET EPA WQX GWML WDTF WFD schemas eWater (EU) SANDRE SANDRE XML Surface water & groundwater “standards” Integration support Standard users Standard developers ODM WaterML (CUAHSI)

CSIRO Cox/TDWG 2008 Observations

CSIRO Cox/TDWG 2008 Our Science is changing: scale From small scale siloed studies To Integration on a global scale Atom Molecule Mineral Rock Outcrop Section Mountain Continent Planet Source: Office of Integrative Activities NSF

CSIRO Cox/TDWG 2008 Our Science is changing: interdisciplinary Source: US Global Change Research Program

CSIRO Cox/TDWG 2008 Ontological value of the Observations & Measurements standard Two user-managed class hierarchies in GFM-based specs: Feature and FeaturesCollection: a Feature-type is characterized by a specific set of properties Up to five user-managed class hierarchies in O&M-based specs Observation, SamplingFeature, PropertyType, Procedure and Result An Observation is an Event whose result is an estimate of the value of some Property of the Feature-of-interest, obtained using a specified Procedure Stronger ontological value for O&M More branches and separation of concern: Example: Difference between Feature and SamplingFeature Feature for the real world objects e.g. an aquifer SamplingFeature to characterise how a measure is done e.g. along a borehole

CSIRO Cox/TDWG 2008 Normalised ontology skeleton for water observation vocabularies Define the right branches at the top Isolate unambiguous primitives (e.g. units) Use modules/namespace/URIs to position source-specific definitions against common ones

CSIRO Cox/TDWG 2008 Observation data interface OGC “Sensor Observation Service” http interface to sensor observations c.f. WFS, WCS, WMS Request parameters scoped by O&M model featureOfInterest observedProperty Procedure Response is XML-encoded O&M

CSIRO Cox/TDWG 2008 Sampling features

CSIRO Cox/TDWG 2008 Proximate vs ultimate feature-of-interest Ultimate (“project”) thing of interest often not directly or fully accessible 1.Proximate feature of interest embodies a sample design Rock-specimen samples an ore-body or geologic unit Well samples an aquifer Profile samples an ocean/atmosphere column Cross-section samples a rock-unit 2.Sensed property is a proxy e.g. want land-cover, but observe colour Some sampling designs are common across disciplines

CSIRO Cox/TDWG 2008 Examples

CSIRO Cox/TDWG 2008 Water quality of aquifers observed in wells

CSIRO Cox/TDWG 2008 Water quality measured along a ferry track

CSIRO Cox/TDWG 2008 Patterns? Much of the interest concerns relations between sampling features, associations with the domain (sampled) features i.e. sampling regimes are core

CSIRO Cox/TDWG 2008 Governance

CSIRO Cox/TDWG 2008 OGC Sensor Web Enablement OGC Web Services testbeds OWS – OWS Core elements of OGC SWE suite SensorML – provider-centric information viewpoint O&M – consumer-centric information viewpoint SOS, SAS – http interface to observations SPS – tasking interface sweCommon – data-types & encodings, including coverage encoding TML – low-level sensor streams

CSIRO Cox/TDWG 2008 SOS getObservation getResult describeSensor getFeatureOfInterest Accessing data using the “Observation” viewpoint WFS/ Obs getFeature, type=Observation WCS getCoverage getCoverage (result) Sensor Register getRecordById WFS getFeature e.g. SOS::getResult == “convenience” interface for WCS

CSIRO Cox/TDWG 2008 WFS/ SFS Accessing data using the “Sampling Feature Service” viewpoint WFS getFeature WCS getCoverage getCoverage (property value) SOS getObservation Common data source getFeature (sampling Feature) getFeature (coverage property value) getFeature (relatedObservation) getCoverage (result) Sensor Register getRecordById (procedure) getFeature (featureOfInterest) getObservation (relatedObs) getResult (property value)

CSIRO Cox/TDWG 2008 WFS Accessing data using the “Domain Feature” viewpoint WCS getCoverage (property value) getFeature SOS getResult (property value) The “George Percivall preferred™” viewpoint #1 – observations are property-value-providers for features ??

CSIRO Cox/TDWG 2008 WCS Accessing data using the “just the data” viewpoint WFS getFeature/geometry (domain exent) getCoverage SOS getResult (lots of ‘em) (range values) The “George Percivall preferred™” viewpoint #2 – observations are range-value-providers for coverages

CSIRO Cox/TDWG 2008 need information transfer standards for Geochemistry Geochronology Geophysics Geodesy Seismology Hydrogeology Marine Ecology Biogeology But need to coordinate these standards (including ontologies) to avoid uncontrolled growth of YAML (Yet Another Markup Language) Application to other science disciplines

CSIRO Cox/TDWG 2008 Procedure vs. observedProperty observedProperty supports discovery by observation users “show me all the observations of temperature and wind-speed” procedure provides strict definition “how was that value obtained?” …or provider-centric discovery “show me all the data collected by instrument X”

CSIRO Cox/TDWG 2008 Some properties vary within a feature colour of a Scene or Swath varies with position shape of a Glacier varies with time flow at a Station varies with time rock density varies along a Borehole Variable values may be described as a Function on some axis of the feature Corresponding Observation/result is a Function If domain is spatio-temporal, also known as coverage or map

CSIRO Cox/TDWG 2008 Variable property  coverage valued result