Conference PaperPDF Available

Pitfalls in alignment of observation models resolved using PROV as an upper ontology

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Abstract and Figures

A number of models for observation metadata have been developed in the earth and environmental science communities, including OGC’s Observations and Measurements (O&M), the ecosystems community’s Extensible Observation Ontology (OBOE), the W3C’s Semantic Sensor Network Ontology (SSNO), and the CUAHSI/NSF Observations Data Model v2 (ODM2). In order to combine data formalized in the various models, mappings between these must be developed. In some cases this is straightforward: since ODM2 took O&M as its starting point, their terminology is almost completely aligned. In the eco-informatics world observations are almost never made in isolation of other observations, so OBOE pays particular attention to groupings, with multiple atomic ‘Measurements’ in each oboe:Observation which does not have a result of its own and thus plays a different role to an om:Observation. And while SSN also adopted terminology from O&M, mapping is confounded by the fact that SSN uses DOLCE as its foundation and places ssn:Observations as ‘Social Objects’ which are explicitly disjoint from ‘Events’, while O&M is formalized as part of the ISO/TC 211 harmonised (UML) model and sees om:Observations as value assignment activities. Foundational ontologies (such as BFO, GFO, UFO or DOLCE) can provide a framework for alignment, but different upper ontologies can be based in profoundly different worldviews and use of incommensurate frameworks can confound rather than help. A potential resolution is provided by comparing recent studies that align SSN and O&M, respectively, with the PROV-O ontology. PROV-O provides just three base classes: Entity, Activity and Agent. om:Observation is sub-classed from prov:Activity, while ssn:Observation is sub-classed from prov:Entity. This confirms that, despite the same name, om:Observation and ssn:Observation denote different aspects of the observation process: the observation event, and the record of the observation event, respectively. Alignment with the simple PROV-O classes has clarified this issue in a way that had previously proved difficult to resolve. The simple 3-class base model from PROV appears to provide just enough logic to serve as a lightweight upper ontology, particularly for workflow or process-based information.
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Pitfallsinalignmentofobservationmodels
resolvedusingPROVasanupperontology
SimonCox|ResearchScientist|EnvironmentalInformatics
16December2015
LANDANDWATER
Overlappingterminology
Sources:
OGCSensorML
OGCObservationsandMeasurements (O&M)
ISOGeneralFeatureModel
SemanticSensorNetworkOntology(SSN)
DOLCEUltraLite
BiologicalCollectionsOntology(BCO)
BasicFormalOntology
Contentiousterms:
Observation
Process
SimonCox‐ AGUFallMeeting2015‐ IN33F07
SensorML‐ Process
SimonCox‐ AGUFallMeeting2015‐ IN33F07
Allcomponents
modeledasprocesses,
including
• Hardware‐ transducers,
sensors,platforms
•Software
Botts & Robin, OGC SensorML – OGC Implementation Specification
OGC document 07-000, 12-000
O&M– Process,Observation
OM_Observation
+ phenomenonTime
+ resultTime
+ validTime [0..1]
+ resultQuality [0..*]
+ parameter [0..*]
GF_PropertyType
GFI_Feature
OM_Process Any
+observedProperty
1
0..*
+featureOfInterest 1
0..*
+procedure
1+result
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
SimonCox‐ AGUFallMeeting2015‐ IN33F07Cox, OGC Abstract Specification – Topic 20: Observations and Measurements 2.0
ISO 19156:2011 Geographic Information – Observations and measurements
‘Observation’producesresult
ataknowntime
Before resultTime:nodata
After resultTime:dataavailable
‘Process’isreusable observation
procedure
omlite <http://def.seegrid.csiro.au/ontology/om/omlite>
SimonCox‐ AGUFallMeeting2015‐ IN33F07S.J.D. Cox, Ontology for observations and sampling features, with alignments to existing
models, Semant. Web J. (2015) Accepted
http://www.semantic-web-journal.net/content/ontology-observations-and-sampling-features-alignments-existing-models-0
SSN– Process,Observation
SimonCox‐ AGUFallMeeting2015‐ IN33F07
Observation,Processboth‘SocialObjects’
Stimulusistheonly‘Event’
M. Compton, P. Barnaghi, L. Bermudez, R. García-Castro, O. Corcho, S.J.D. Cox, et al.,
The SSN ontology of the W3C semantic sensor network incubator group,
Web Semant. Sci. Serv. Agents World Wide Web. 17 (2012) 25–32. doi:10.1016/j.websem.2012.05.003.
Walls RL, Deck J, Guralnick R, Baskauf S, Beaman R, et al. (2014) Semantics in Support of
Biodiversity Knowledge Discovery: An Introduction to the Biological Collections Ontology and
Related Ontologies. PLoS ONE 9(3): e89606. doi:10.1371/journal.pone.0089606
BCO‐ ObservingProcess
ObservingProcess subClassOf* BFO:Occurrent
SimonCox‐ AGUFallMeeting2015‐ IN33F07
Processflowmodel
CorePROV
SimonCox‐ AGUFallMeeting2015‐ IN33F07
Developedprimarilyfordatasets,dataproducts,reports
T. Lebo, S. Sahoo, D.L. McGuinness, PROV-O: The PROV Ontology, (2013).
http://www.w3.org/TR/prov-o/ (accessed February 13, 2014).
CorePROV– alignedwithBFO/BCO
SimonCox‐ AGUFallMeeting2015‐ IN33F07
bfo:Occurrent
??
bfo:Continuant
bco:ObservingProcess
CorePROV– alignment withO&M
SimonCox‐ AGUFallMeeting2015‐ IN33F07
om:Observation
om:Process
om:Result
CorePROV– alignmentwithSSN
SimonCox‐ AGUFallMeeting2015‐ IN33F07
??
ssn:Sensor
ssn:Observation
SSNXalignedwithPROV
SimonCox‐ AGUFallMeeting2015‐ IN33F07
M. Compton, D. Corsar, K. Taylor, Sensor Data Provenance:
SSNO and PROV-O Together at Last,
in: 7th Int. Work. Semant. Sens. Networks, 2014.
CorePROV– alignmentwithSSNX
SimonCox‐ AGUFallMeeting2015‐ IN33F07
ssnx:ActivityOfSensing
ssn:Sensor
ssn:Observation
Relates to sensor as an asset?
bfo:Continuant
CorePROV– allalignments
SimonCox‐ AGUFallMeeting2015‐ IN33F07
ssnx:ActivityOfSensing
ssn:Sensor
ssn:Observation
bfo:Occurrent
bco:ObservingProcess
om:Observation
om:Process
Generationofobservationdatamatchesagenericprocessmodel
PROVisaconvenientupperontologyforalignments
Reusable agents
SamplingFeatures‐ samliteontology
SimonCox‐ AGUFallMeeting2015‐ IN33F07S.J.D. Cox, Ontology for observations and sampling features, with alignments to existing
models, Semant. Web J. (2015) Accepted
http://www.semantic-web-journal.net/content/ontology-observations-and-sampling-features-alignments-existing-models-0
CorePROV– alignmentwithSpecimenprep
SimonCox‐ AGUFallMeeting2015‐ IN33F07
sam:Process
sam:Specimen
sam:PreparationStep
Specimenpreparationandobservationtrace
Lifecycleeventsmodelledas
prov:Activity instances
Analysis
Sieving
Grinding
Splitting
Specimenretrieval
Peopleandmachinesmodelled
asprov:Agent instances
LabTec h, Geologist
Sievestack
Mill
Saw
Hammer
SimonCox‐ AGUFallMeeting2015‐ IN33F07Cox, SJD & Car, NJ Provenance of things - describing geochemistry
observation workflows using PROV-O, IN33A-1784
Otheralignmentsand
extensions
prov:Entity ←:PhysicalEntity ←:Specimen
prov:Entity ←prov:Plan ←:SamplingProtocol
prov:Agent ←:SampleProcessingSystem
←:GrindingSystem,:PolishingSystem,:DissolvingSystem,:FusingSystem
prov:Agent ←:SampleRetrievalSystem ←:FieldSamplingSystem
prov:Agent ←:SubSamplingSystem
←:BiasedSplittingSystem
←:SizeSeparationSystem ,:DensitySeparationSystem,:MagneticSeparationSystem
prov:Agent ←Instrument,Sensor
prov:wasAssociatedWith ←:wasControlledBy,:wasSponsoredBy,:wasRequestedBy
prov:wasDerivedFrom ←:unbiasedSplitFrom,:biasedSplitFrom
prov:wasDerivedFrom ←prov:hadPrimarySource ←:fieldSpecimen
SimonCox‐ AGUFallMeeting2015‐ IN33F07
Summary‐ inpraiseofPROV
Observationmodels/ontologiesuseterms“observationand“process”
•Intercommunitydiscussionsarevulnerabletomisunderstandings
Groundingintraditional‘upperontologies’doesn’tnecessarilyhelp!
Generatingresultsofobservationsisessentiallyaprocesschain
PROVprovidesalightweight‘upperontology’thatcanhelp
SimonCox‐ AGUFallMeeting2015‐ IN33F07
LANDANDWATER
Thankyou
CSIROLandandWater
SimonCox
ResearchScientist
t+61392526342
esimon.cox@csiro.au
wwww.csiro.au/people/simon.cox
OBOEobservationmodel
SimonCox‐ AGUFallMeeting2015‐ IN33F07
OneObservationis
composedofmultiple
Measurements
Eachforadifferent
Characteristic ofthe
sameEntity
OBOEobservationmodel
SimonCox‐ AGUFallMeeting2015‐ IN33F07
SimonCox‐ AGUFallMeeting2015‐ IN33F07
om:ObservationCollection oboe:Observation
commonfeatureofinterest,phenomenonTime
om:Observationoboe:Measurement
featureofinterest,phenomenonTime fromcollection
Article
Full-text available
Earth System Science (ESS) observational data are often inadequately semantically enriched by geo-observational information systems to capture the true meaning of the associated data sets. Data models underpinning these information systems are often too rigid in their data representation to allow for the ever-changing and evolving nature of ESS domain concepts. This impoverished approach to observational data representation reduces the ability of multi-disciplinary practitioners to share information in a computable way. Object oriented techniques that are typically employed to model data in a complex domain (with evolving domain concepts) can unnecessarily exclude domain specialists from the design process, invariably leading to a mismatch between the needs of the domain specialists, and how the concepts are modelled. In many cases, an over simplification of the domain concept is captured by the computer scientist. This paper proposes that two-level modelling methodologies developed by health informaticians to tackle problems of domain specific use-case knowledge modelling can be re-used within ESS informatics. A translational approach to enable a two-level modelling process within geo-observational sensor systems design is described. We show how the Open Geospatial Consortium’s (OGC) Observations & Measurements (O&M) standard can act as a pragmatic solution for a stable reference-model (necessary for two-level modelling), and upon which more volatile domain specific concepts can be defined and managed using archetypes. A rudimentary use-case is presented, followed by a worked example showing the implementation methodology and considerations leading to an O&M based, two-level modelling design approach, to realise semantically rich and interoperable Earth System Science based geo-observational sensor systems.
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