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Human-Aware Sensor Network Ontology (HASNetO): Semantic Support for Empirical Data Collection

Human-Aware Sensor Network Ontology (HASNetO): Semantic Support for Empirical Data Collection. Paulo Pinheiro 1 , Deborah McGuinness 1 , Henrique Santos 1,2 1 Rensselaer Polytechnic Institute, USA 2 Universidade de Fortaleza, Brazil ISWC/LISC, October 2015. Outline.

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Human-Aware Sensor Network Ontology (HASNetO): Semantic Support for Empirical Data Collection

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  1. Human-Aware Sensor Network Ontology (HASNetO): Semantic Support for Empirical Data Collection Paulo Pinheiro1, Deborah McGuinness1, Henrique Santos1,2 1Rensselaer Polytechnic Institute, USA 2Universidade de Fortaleza, Brazil ISWC/LISC, October 2015

  2. Outline • Capturing Contextual Knowledge • Integration of Empirical Concepts and Sensor Network Concepts • Provenance Knowledge support for Contextual Knowledge • HASNetO: The Human-Aware Sensor Network Ontology • Conclusions

  3. Knowledge Capture technician scientist reports needs maintains (deploys, calibrates) Sensor network uses Individual Instrument(s) senses senses senses measurement Data (e.g., CSV file) measurement data Database interactions queries data flows data user (including scientists)

  4. Measurement Time Interval A Comma-Separated Value (CSV) dataset: TimeStamp,AirTemp_C_Avg,RH_Pct_Avg 2015-02-12T09:30:00Z,-4.5,66.58 2015-02-12T09:45:00Z,-4.372,66.45 2015-02-12T10:00:00Z,-4.146,65.98 2015-02-12T10:15:00Z,-4.084,66.22 2015-02-12T10:30:00Z,-4.251,67.48 2015-02-12T10:45:00Z,-4.185,69.85 2015-02-12T11:00:00Z,-4.133,72 2015-02-12T11:15:00Z,-3.959,70.84 … 2015-02-12T23:00:00Z,-9.63,77.88 2015-02-12T23:15:00Z,-10.48,80.8 2015-02-12T23:30:00Z,-10.96,82 2015-02-12T23:45:00Z,-10.1,80.7 February 12, 2015, 11:45PM February 12, 2015, 9:30AM t

  5. Temporal Contextual Diff Sensor Calibration Configuration Infrastructure Acquisition Deployment Data usage t February 12, 2015, 9:30AM February 12, 2015, 11:45PM t

  6. Full Extent of Contextual Knowledge Scope trust agents space time “typical” measurement scope

  7. Selected Observation and Sensor Network Ontologies • Sensor Network Knowledge • Needed to describe the infrastructure of a sensor network, and the use of sensor network components in the generation of datasets • Observation Knowledge • Needed to describe observations and their measurements. Measurements need to be characterized in terms of physical entities, entity characteristics, units, and values

  8. Observation Concepts In our measurements, observation concepts are either OBOE concepts or OBOE-derived concepts. The thing that one is observing is an entity, e.g.,’air’. Things that are observed, however, cannot be measured. For example, how can one measure ‘air’? A characteristic is a measurable property of an entity, e.g., air temperature. An observation is a collection of measurements of entity’s characteristics. Each measurement has a value, e.g, ’45’, and a standard unit, e.g., ‘Celsius’. oboe: Observation of-entity oboe: Entity 1 1 1 1 hasneto: hasContext * hasneto: DataCollection has-characteristic 1 hasneto: hasMeasurement * * 1 * oboe: Measurement oboe: Characteristic of-characteristic 1 has-characteristic-value * has-value uses-standard 1 1 * 1 * oboe: Standard oboe: Value has-standard-value

  9. Sensor Network Concepts In the Jefferson Project, sensor network concepts are either Virtual Solar-Terrestrial Observatory (VSTO) concepts or VSTO-derived concepts. Instruments and their detectors are used to perform measurements. Instruments, however, can only perform measurements during a deployment at a given platform, e.g., tower, plane, person, buoy hasneto: Sensing Perspective 1 * * oboe: Characteristic vstoi: Detector hasPerspective Characteristic perspectiveOf * 0..1 vstoi: Attached Detector vstoi: Detachable Detector * vstoi: Platform oboe: Entity vstoi: Instrument

  10. Selected Provenance Ontology Provenance Knowledge is needed to contextualize VTSO deployments and OBOE observations • “Who deployed an instrument?” • “When was the instrument deployed?” • “How many times instrument parameters changed during deployment?” • “What was the value of each parameter during a given observation?”

  11. W3C PROV Concepts Provenance concepts are W3C PROV concepts.

  12. Provenance-Level Integration • Provenance provides contextual high-level integration of observation and sensor network concepts • Integration also occurs in terms of information flow allowing full accountability of measurements in the context of sensor network components and configurations wasDerivedFrom wasAttributeTo prov: Entity actedOnBehalfOf wasGeneratedBy prov: Agent used startedAtTime xsd:dateTime prov: Activity wasAssociatedWith xsd:dateTime endedAtTime hasData Collection hasneto: DataCollection vstoi: Deployment 1 *

  13. The Human-Aware Sensor Network Ontology wasAssociatedWith prov: Agent startedAtTime prov: Activity xsd:dateTime 1 vstoi: Platform endedAtTime xsd:dateTime * hasData Collection * hasneto: DataCollection vstoi: Deployment 1 vstoi: Instrument 1 * * hasneto: hasMeasurement 1 * * hasneto: Sensing Perspective * oboe: Measurement vstoi: Detector perspectiveOf * * hasPerspective Characteristic of-characteristic * 1 1 vstoi: Attached Detector vstoi: Detachable Detector 0..1 * oboe: Characteristic oboe: Entity

  14. Metadata in Action Mouse over

  15. Combining Data and Metadata Measurement metadata Mouse over Metadata based faceted search Mouse over Metadata about the metadata

  16. Conclusions • HASNetO was briefly presented along with its support for describing sensor networks • OBOE and VSTO provide concepts required for encoding observation and sensor network metadata • Neither OBOE and VSTO provide concepts for describing contextual knowledge about deployments and observations HASNetO provides a comprehensive integrated set of concepts for capturing sensor network measurements along with contextual knowledge about these measurements

  17. Extra

  18. SPARQL Queries Against HASNetO • Question in English: “List detectors currently deployed with instrument vaisalaAW310-SN000000 and the physical characteristics measured by these detectors” • W3C SPARQL query (a translation of the question above): select ?detector ?characteristic ?platform where { ?deployment a Deployment>. ?deployment vsto:hasInstrument kb:vaisalaAW310-SN000000. ?platform vsto:hasDeployment ?deployment. ?deployment hasneto:hasDetector ?detector. ?detector oboe:detectsCharacteristic ?characteristic. } • Query Result: +----------------+-------------------+--------------------+ | detector | characteristic | platform | +----------------+-------------------+--------------------+ | Vaisala WMT52 | windSpeed | towerDomeIsland | +----------------+-------------------+--------------------+

  19. Example of a HASNetO Knowledge Base* :obs1 a oboe:Observation; oboe:ofEntity oboe:air; prov:startedAtTime "2014-02-11T01:01:01Z"^^xsd:dateTime; prov:endedAtTime "2014-02-12T01:01:01Z"^^xsd:dateTime; . :dp1 a vsto:Deployment; vsto:hasInstrument :vaisalaAW310-SN000000; hasneto:hasDetector :vaisalaWMT52-SN000000; hasneto:hasObservation :obs1; prov:startedAtTime "2014-02-10T01:01:01Z"^^xsd:dateTime; prov:endedAtTime "2014-02-17T01:20:02Z"^^xsd:dateTime; . :genericTower vsto:hasDeployment :dp1; . :dset1 a vsto:Dataset; prov:wasAttributedTo :vaisalaAW310; prov:wasGeneratedBy :obs1; . *The knowledge base fragment above is represented in W3C Turtle.

  20. Knowledge About Sensor Network Operation • Knowledge about sensor networks, however, can rarely be inferred from sensor data themselves. • The lack of contextual knowledge about sensor data can render them useless. Knowledge about sensor networks is as important as data captured by sensor networks, and sensor network metadata is as important as sensor data

  21. Human-Aware Data Acquisition Framework • Two locations: • Darrin Fresh Water Institute (DFWI) at Lake George, NY and • data processing site in Troy, NY • Wireless network used to communicate with sensors • Relational database for data management and RDF triple store for metadata management 20

  22. Future Steps • We will keep refining the HASNetO vocabulary and testing it over a constantly growing HASNetO-based knowledge base • We are in the process of integrating HASNetO into the HAScO (Human-Aware Science Ontology) to accommodate contextual knowledge beyond observation data to include simulation data and experimental data

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