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Ontology alignment – is PROV-O good enough?

Presentation to OGC Geosemantics Summit, 2015-06-03.
I explain the incompatibiity between the Observation classes in SSN and O&M, and how this can be understood mostly clearly through alignment with PROV. Compared with other 'upper ontologies' PROV provides a very easy to understand framework, with only 3 top level classes, two of which are disjoint.

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Ontology alignment – is PROV-O good enough?

  1. 1. Ontology alignment – is PROV-O good enough? Simon Cox | Research Scientist | Environmental Information Infrastructures 3 June 2015 LAND AND WATER Geosemantics Summit – OGC TC, Boulder, CO, USA - 2015-06-03
  2. 2. Outline • Upper ontologies • O&M vs SSN • Alignment strategy Ontology alignment using PROV | Simon Cox2 |
  3. 3. Ontology alignment using PROV | Simon Cox3 | Upper ontologies http://www.disi.unige.it/person/MascardiV/Download/DISI-TR-06-21.pdf
  4. 4. General Formal Ontology Ontology alignment using PROV | Simon Cox4 |
  5. 5. Basic Formal Ontology Ontology alignment using PROV | Simon Cox5 |
  6. 6. DOLCE ultra-lite Ontology alignment using PROV | Simon Cox6 |
  7. 7. Ontology recapitulates ideology Ontology alignment using PROV | Simon Cox7 |
  8. 8. PROV Ontology alignment using PROV | Simon Cox8 |
  9. 9. OM_Observation + phenomenonTime + resultTime + validTime [0..1] + resultQuality [0..*] + parameter [0..*] GF_PropertyType GFI_Feature OM_Process Any +observedProperty 1 0..* +featureOfInterest 1 0..* +procedure1 +result Range 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 ISO 19156 Observations and Measurements 9 | Ontology alignment using PROV | Simon Cox Cox, OGC Abstract Specification – Topic 20: Observations and Measurements 2.0 ISO 19156:2011 Geographic Information – Observations and measurements
  10. 10. om:Observation using ISO 19105-2 rule Ontology alignment using PROV | Simon Cox10 | (TopBraid diagram view)
  11. 11. O&M integrated into ISO framework Ontology alignment using PROV | Simon Cox11 | Direct ISO dependencies • gf - Feature ISO 19109 • cv - Coverage (fields) ISO 19123 • md - Metadata ISO 19115 • gm - Geometry ISO 19107 • tm - Temporal ISO 19108 • h2o - Meta-model ISO 19150-2 • basic - Datatypes ISO 19103 Required ISO UML models converted to OWL No other alignment attempted at this time
  12. 12. om-lite Ontology alignment using PROV | Simon Cox12 | Sem. Web. Journal – in review
  13. 13. SSN Ontology Ontology alignment using PROV | Simon Cox13 |
  14. 14. SSN aligned with DOLCE ultra-lite Ontology alignment using PROV | Simon Cox14 | Is Observation a Social Object, or an Event? Sensing vs. Reasoning?
  15. 15. PROV Ontology alignment using PROV | Simon Cox15 |
  16. 16. Ontology alignment using PROV | Simon Cox16 | Compton, Corsar & Taylor, Sensor Data Provenance: SSNO and PROV-O Together at Last. SSN2014
  17. 17. om-lite aligned to PROV-O Ontology alignment using PROV | Simon Cox17 | Cox, Basic Observations and Sampling Feature Ontology . Semantic Web J. (submitted)
  18. 18. SSN Observation vs O&M Observation ssn:Observation subClassOf prov:Entity BFO Continuant om:Observation subClassOf prov:Activity BFO Occurrent Ontology alignment using PROV | Simon Cox18 | ssn:Observation om:Observation
  19. 19. Conclusion Term “Observation” is being used for different concepts in SSN cf O&M Record of observation vs. observation event ? Prov-O helps clarify, without getting tangled up in formal ‘Upper Ontologies’ Ontology alignment using PROV | Simon Cox19 | ssn:Observation om:Observation new:ActivityOfSensing om:Process ssn:Sensor om:Result ssn:SensorOutput
  20. 20. LAND AND WATER Thank youCSIRO Land and Water Simon Cox Research Scientist t +61 3 9252 6342 e simon.cox@csiro.au w www.csiro.au/people/simon.cox
  21. 21. The need for standardisation • Integrated modelling is becoming the norm • bioregional assessment • eReefs • When using heterogeneous (data) sources, discovery & integration is a major challenge • Standards make this easier Many private contracts one public agreement Ontology alignment using PROV | Simon Cox21 | Remote sensing Sensor Value Parameter Scene Earth science Algorithm, code, simulator Model, field Variable Volume, grid Metrology Instrument Value Measurand Sample Chemistry Instrument, analytical process Analysis Analyte Sample Environmental monitoring Gauge, sensor Value, time-series Parameter Station Observations & Measurements procedure result observed property feature of interest
  22. 22. Views of data Continuous phenomena, varying in space and time – ‘raster’. A function: spatial, temporal or spatio- temporal domain to attribute range Ontology alignment using PROV | Simon Cox Features Features exist, have attributes and can be spatially described – ‘discrete’ or ‘vector’ Coverages Observations An act that results in the estimation of the value of a feature property, and involves application of a specified procedure, such as a sensor, instrument, algorithm or process chain 22 |

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