1.
Human-Aware Sensor Network Ontology
(HASNetO): Semantic Support for Empirical
Data Collection
Paulo Pinheiro1
, Deborah McGuinness1
,
Henrique Santos1,2
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
2

3.
DatabaseDatabase
Sensor
network
technician scientist
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
senses
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
6
time
spaceagentstrust
“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
11
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
11
*
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
12
prov:
Activity
hasneto:
DataCollection
vstoi:
Deployment
xsd:dateTime
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
xsd:dateTime
hasData
Collection
1*
prov:
Agent
wasAssociatedWith
startedAtTime
endedAtTime
1
1
*
*
*
*
oboe:
Measurement
of-characteristic
hasneto:
hasMeasurement 1
1
*
*

14.
Metadata in Action
14
Mouse over

15.
Combining Data and
Metadata
15
Mouse over
Mouse over
M
etadata
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
16
HASNetO provides a comprehensive integrated
set of concepts for capturing sensor network
measurements along with contextual knowledge
about these measurements

17.
• Extra
17

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 |
+----------------+-------------------+--------------------+
18

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

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
22