pitfalls in alignment of observation models resolved using prov as an upper ontology

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Pitfalls in alignment of observation models resolved using PROV as an upper ontology Simon Cox | Research Scientist | Environmental Informatics16 December2015Land and water

In order for the vision of the semantic web to be fulfilled, ontologies developed for related applications and deployed by different organizations must be aligned. Some ontologies are tied to a particular foundation ontology (such as BFO, GFO, UFO or DOLCE) by design, with their key classes in a subsumption hierarchy derived from the upper ontology. This enables rapid alignment with other ontologies developed in the same framework, but can present barriers otherwise.The Semantic Sensor Network ontology (SSN) was developed through a W3C incubator group with the goal of providing a general purpose ontology for sensor-based observations. It has seen widespread adoption in the semantic web community. On the other hand, in the geospatial community, OGC Observations and Measurements (O&M) provides the model used for standard observation services. While SSN adopts much of the terminology from O&M, full alignment has been a challenge. This is confounded by the fact that each is rooted in a different base ontology SSN uses DOLCE as its foundation, while O&M is formalized as part of the ISO/TC 211 harmonised (UML) model. While some conflict derives from the tension between the frame-based UML paradigm used for O&M and the open-world, description-logics based paradigm used in SSN, this does not appear to be the whole story.A possible 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 implies 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. 1

Overlapping terminologySources: OGC SensorMLOGC Observations and Measurements (O&M) ISO General Feature ModelSemantic Sensor Network Ontology (SSN) DOLCE UltraLiteBiological Collections Ontology (BCO) Basic Formal Ontology

Contentious terms:ObservationProcessSimon Cox - AGU Fall Meeting 2015 - IN33F-07

SensorML - Process

Simon Cox - AGU Fall Meeting 2015 - IN33F-07 All components modeled as processes, including Hardware - transducers, sensors, platformsSoftwareBotts & 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

+observedProperty1

0..*+featureOfInterest1

0..*+procedure1

+resultAn 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

Simon Cox - AGU Fall Meeting 2015 - IN33F-07 Cox, OGC Abstract Specification Topic 20: Observations and Measurements 2.0 ISO 19156:2011 Geographic Information Observations and measurements

Observation produces result at a known timeBefore resultTime: no dataAfter resultTime: data available

Process is reusable observation procedure

4Cutting to the chase, working primarily in OGC we developed a standard for Observations and Measurements. It was published in 2011 as an ISO standard

The core story is this: an information model, specified in UML, which defines a small terminology to describe the key aspects of an observation.

The terminology is deliberately domain neutral, and the terms all have (different) local equivalents in different domain applications.

The key trick was to recognize that the observation is a separate concept to the observation-result, and that by also treating the descriptions of the procedure and the feature of interest separately, we get a very general framework which covers

om-lite Simon Cox - AGU Fall Meeting 2015 - IN33F-07

S.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

5

SSN Process, Observation

Simon Cox - AGU Fall Meeting 2015 - IN33F-07

Observation, Process both Social ObjectsStimulus is the only EventM. Compton, P. Barnaghi, L. Bermudez, R. Garca-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) 2532. 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.0089606BCO - ObservingProcess ObservingProcess subClassOf* BFO:Occurrent

Simon Cox - AGU Fall Meeting 2015 - IN33F-07

Process-flow model Core PROVSimon Cox - AGU Fall Meeting 2015 - IN33F-07 Developed primarily for datasets, data products, 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).

Core PROV aligned with BFO/BCOSimon Cox - AGU Fall Meeting 2015 - IN33F-07 bfo:Occurrent??bfo:Continuantbco:ObservingProcess

Core PROV alignment with O&MSimon Cox - AGU Fall Meeting 2015 - IN33F-07 om:Observationom:Processom:Result

Core PROV alignment with SSNSimon Cox - AGU Fall Meeting 2015 - IN33F-07 ??ssn:Sensorssn:Observation

SSNX aligned with PROVSimon Cox - AGU Fall Meeting 2015 - IN33F-07

M. Compton, D. Corsar, K. Taylor, Sensor Data Provenance: SSNO and PROV-O Together at Last, in: 7th Int. Work. Semant. Sens. Networks, 2014.

Core PROV alignment with SSNXSimon Cox - AGU Fall Meeting 2015 - IN33F-07 ssnx:ActivityOfSensingssn:Sensorssn:Observation

Relates to sensor as an asset?

bfo:ContinuantCore PROV all alignmentsSimon Cox - AGU Fall Meeting 2015 - IN33F-07 ssnx:ActivityOfSensingssn:Sensorssn:Observationbfo:Occurrentbco:ObservingProcessom:Observationom:ProcessGeneration of observation data matches a generic process model PROV is a convenient upper-ontology for alignments

Reusable agents

Sampling Features - sam-lite ontologySimon Cox - AGU Fall Meeting 2015 - IN33F-07

S.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

Core PROV alignment with Specimen prep Simon Cox - AGU Fall Meeting 2015 - IN33F-07

sam:Processsam:Specimensam:PreparationStep

Specimen preparation and observation traceLifecycle events modelled as prov:Activity instancesAnalysisSievingGrindingSplittingSpecimen retrieval

People and machines modelled as prov:Agent instancesLab Tech, GeologistSieve stackMillSawHammer

Simon Cox - AGU Fall Meeting 2015 - IN33F-07

Cox, SJD & Car, NJ Provenance of things - describing geochemistry observation workflows using PROV-O, IN33A-1784

Other alignments and extensionsprov:Entity :PhysicalEntity :Specimen prov:Entity prov:Plan :SamplingProtocol

prov:Agent :SampleProcessingSystem :GrindingSystem, :PolishingSystem, :DissolvingSystem, :FusingSystemprov:Agent :SampleRetrievalSystem :FieldSamplingSystemprov:Agent :SubSamplingSystem :BiasedSplittingSystem :SizeSeparationSystem , :DensitySeparationSystem, :MagneticSeparationSystemprov:Agent Instrument , Sensorprov:wasAssociatedWith :wasControlledBy, :wasSponsoredBy, :wasRequestedByprov:wasDerivedFrom :unbiasedSplitFrom, :biasedSplitFromprov:wasDerivedFrom prov:hadPrimarySource :fieldSpecimen

Simon Cox - AGU Fall Meeting 2015 - IN33F-07

Summary - in praise of PROV Observation models/ontologies use terms observation and process Inter-community discussions are vulnerable to misunderstandingsGrounding in traditional upper ontologies doesnt necessarily help!

Generating results of observations is essentially a process-chain PROV provides a lightweight upper ontology that can helpSimon Cox - AGU Fall Meeting 2015 - IN33F-07

Land and waterThank youCSIRO Land and WaterSimon CoxResearch Scientistt+61 3 9252 6342esimon.cox@csiro.auwwww.csiro.au/people/simon.cox

OBOE observation model

Simon Cox - AGU Fall Meeting 2015 - IN33F-07 One Observation is composed of multiple MeasurementsEach for a different Characteristic of the same Entity

OBOE observation model

Simon Cox - AGU Fall Meeting 2015 - IN33F-07

Simon Cox - AGU Fall Meeting 2015 - IN33F-07

om:ObservationCollection oboe:Observationcommon feature-of-interest, phenomenonTimeom:Observation oboe:Measurement feature-of-interest, phenomenonTime from collection