1. 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

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 1

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

4. Measurement Time Interval 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 t A Comma-Separated Value (CSV) dataset: February 12, 2015, 9:30AM February 12, 2015, 11:45PM

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

6. Full Extent of Contextual Knowledge Scope 5 time space agentstrust “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: Entity oboe: Observation of-entity 1 1 hasneto: DataCollection oboe: Measurement oboe: Standard oboe: Characteristic oboe: Value of-characteristic hasneto: hasMeasurement uses-standard has-characteristic has-characteristic-value has-standard-value has-value hasneto: hasContext 1 1 * 1 1 1 1 1 1 * * * * * *

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 vstoi: Detector vstoi: Instrument vstoi: Platform hasneto: Sensing Perspective oboe: Characteristic oboe: Entity vstoi: Detachable Detector vstoi: Attached Detector ** * 1 0..1 * hasPerspective Characteristic perspectiveOf

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 11 prov: Activity hasneto: DataCollection vstoi: Deployment xsd:dateTime hasData Collection 1 * prov: Agent prov: Entity used wasGeneratedBy wasAttributeTo wasAssociatedWith actedOnBehalfOf wasDerivedFrom startedAtTime endedAtTime

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

14. Metadata in Action 13 Mouse over

15. Combining Data and Metadata 14 Mouse over Metadata based faceted search Measurement metadata 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 15 HASNetO provides a comprehensive integrated set of concepts for capturing sensor network measurements along with contextual knowledge about these measurements

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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 | +----------------+-------------------+--------------------+ 17

19. Example of a HASNetO Knowledge Base* 18 : 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. 20 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

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 21