=Paper=
{{Paper
|id=None
|storemode=property
|title=What Now and Where Next for the W3C Semantic Sensor Networks Incubator Group Sensor Ontology
|pdfUrl=https://ceur-ws.org/Vol-839/compton.pdf
|volume=Vol-839
|dblpUrl=https://dblp.org/rec/conf/semweb/Compton11
}}
==What Now and Where Next for the W3C Semantic Sensor Networks Incubator Group Sensor Ontology==
https://ceur-ws.org/Vol-839/compton.pdf
What Now and Where Next for the W3C
Semantic Sensor Networks Incubator Group
Sensor Ontology
Michael Compton
CSIRO ICT Centre,
Canberra, Australia
Michael.Compton@CSIRO.AU
Abstract. This short paper accompanies the keynote given at SSN’11.
It reviews the initiation of the Semantic Sensor Networks Incubator
Group and the ontology produced. Also, examples of the use of the ontol-
ogy, potential extensions and options for future use are briefly discussed.
The ontology is available at:
http://purl.oclc.org/NET/ssnx/ssn.
1 The SSN-XG and the Sensor Ontology
The Semantic Sensor Networks Incubator Group (the SSN-XG) was formed by
CSIRO, Wright State University and the OGC in early 2009, formally commenc-
ing on March 4, 2009. The group’s charter
http://www.w3.org/2005/Incubator/ssn/charter
lists the development of an ontology for sensors and semantic annotations as its
two key areas of work. This paper discusses only the work on ontologies and the
resulting SSN ontology.
At the the group’s inception, there was already interest in semantic sensor
networks, including a number papers and ontologies for sensors, reviewed by the
group [5], as well as projects such as SemsorGrid4Env1 and SENSEI.2 Further,
there was a growing realisation that semantics could complement and enhance
standards, such as the OGC SWE suite (in particular, in this context, Sen-
sorML [2] and O&M [6,7]), that largely provide syntactic interoperability; see,
for example, the analysis by Cameron et al. [3]. Indeed, the notion of a Semantic
Sensor Web [9] had already been developed.
The introduction of a Web of things and linked knowledge fragments, that
interacts with and represents the real world, presented a further vision for se-
mantic sensor networks, in which sensors are things that observe other things.
1
http://www.semsorgrid4env.eu/
2
http://www.sensei-project.eu/
The possible size, complexities and heterogeneity of such a Web indicates po-
tential for specification, search, linking, reasoning, and the like, all supported
by semantics. Indeed, the SSN-XG charter states that “A semantic sensor net-
work will allow the network, its sensors and the resulting data to be organised,
installed and managed, queried, understood and controlled through high-level
specifications.”
The SSN-XG closed in September 2010, with 41 people from 16 organisations
having joined the group. 24 people are credited in the SSN-XG Final Report.
The group met weekly via teleconference and once face to face, coinciding with
ISWC/SSN 2009 in Washington. The group’s members represented universities,
research institutes and multinationals. The activities of the group are recorded
on a wiki
http://www.w3.org/2005/Incubator/ssn/wiki/Main_Page,
from which the group’s final report
http://www.w3.org/2005/Incubator/ssn/XGR-ssn/
can also be reached.
1.1 The SSN Ontology — http://purl.oclc.org/NET/ssnx/ssn
Fig. 1. Overview of the SSN ontology and the ten conceptual modules (not all concepts
and properties shown).
The SSN-XG produced an OWL2, SRIQ(D), ontology for describing the
capabilities of sensors, the act of sensing and the resulting observations. The
ontology, called the SSN ontology is available at
http://purl.oclc.org/NET/ssnx/ssn.
The SSN ontology was produced by group consensus; discussion and votes on
extensions were taken at meetings and by email. The ontology has 41 concepts
and 39 object properties, organised into the ten conceptual modules shown in
Figure 1.
Navigable documentation on the group’s wiki
http://www.w3.org/2005/Incubator/ssn/wiki/SSN
is largely automatically derived from the ontology. Each concept and property
is annotated with rdfs:comment, rdfs:isDefinedBy, rdfs:label, rdfs:seeAlso and
dc:source comments, which include SKOS mappings to sources and similar defi-
nitions.
The ontology is aligned to DOLCE UltraLite,3 which further explains con-
cepts and relations and restricts possible interpretations.
The ontology can be seen from four related perspectives: a sensor perspective,
with a focus on what senses, how it senses, and what is sensed; an observation, or
data, perspective, with a focus on observations and related metadata; a system
perspective, with a focus on systems of sensors and deployments; and, a feature
and property perspective, focusing on what senses a particular property or what
observations have been made about a property.
Central to the ontology is the Stimulus-Sensor-Observation (SSO) pattern,
Figures 1 and 2. The SSO pattern is explained next, followed by the four per-
spectives.
Stimulus-Sensor-Observation Pattern The SSO pattern [8] is designed as a
minimal set of concepts, and minimal ontological commitments, that encapsulate
the core concepts of sensing: what senses (Sensors); what is detected (a Stimulus,
that in turn stand for properties of features);4 and what tells us about a sensing
event (Observations).
The pattern can serve as a basis for more complex ontologies, like the full
SSN ontology, is simpler and more easily understandable than the full ontology
and could serve as a minimal ontology for linked sensor data.
Sensor Perspective The SSN ontology takes a liberal view of what can be a
sensor, allowing anything that senses a real-world property using some method.
Hence, devices, whole systems, laboratory set-ups, even biological systems can
all be described as sensors. Sensor is described as skos:exactMatch with sensor
in SensorML and skos:closeMatch with observation procedure O&M.
3
http://www.loa-cnr.it/ontologies/DUL.owl
4
Properties are observable aspects of real world things, while FeaturesOfInterest are
things that we might like to observe properties of: for example, the temperature or
depth (properties) of a lake (a feature).
Fig. 2. The Stimulus-Sensor-Observation pattern. The pattern shows the central role of
stimuli, sensors and observations and how these concepts relate to features, properties
and other key sensing concepts.
The ontology can also be used to describe capabilities of sensors, Figure 3. A
MeasurementCapability specifies, in given conditions, the Accuracy, Detection-
Limit, Drift, Frequency, Latency, MeasurementRange, Precision, Resolution, Re-
sponseTime, Selectivity, and Sensitivity of a sensor. These properties are them-
selves observable aspects of the sensor, given some environmental conditions. For
example, a specification could show that a sensor has accuracy of ±2% in one
condition, but ±5% in another.
Observation Perspective In the SSN ontology, observations are situations
that describe the stimulus and result of sensing, given a sensing method. That
is, observations link the act of sensing, the stimulus event, the sensor, the sensed
property and feature, and a result, placing these in an interpretative context.
Observations are thus an explanation of an observing act and result — not the
event itself. In the DUL alignment they are social constructs (situations).
The Observation concept is described as skos:closeMatch with observation in
O&M. The same data is recorded in both; however, in O&M, an Observation is
the act of sensing and a record of the result.
System Perspective Systems are units of organisation that may have subsys-
tems, maybe attached to platforms and may be deployed, Figure 4. A system has
operating and survival ranges that describe its intended operating conditions and
conditions beyond which it is considered broken. As with MeasurementCapabil-
ity for sensors, OperatingRange and SurvivalRange are observable properties of
systems.
Fig. 3. Sensors are anything that sense: that implement some sensing method. The
capabilities of a sensor are described as observable characteristics (Properties) using
MeasurmentCapability.
Feature and Property Perspective Feature and property are woven through-
out the SSO pattern, the sensor perspective, the observation perspective and the
system perspective. Viewing the world from a feature and property perspective
allows, for example, seeing a knowledge base in terms of questions like what ob-
serves property p, what has observations affected by p, what observations have
been made about p and what devices withstand given environmental extremes.
Examples The SSN ontology doesn’t have concepts for domain, time and place,
features or properties. This additional context is included in the usual OWL way:
import another ontology and show (with subconcepts and equivalent concepts)
how the concepts are aligned. For example, one might import ontologies for fea-
tures (and place these as subconcepts of Feature) and then define (as subconcepts
of Sensor) all the relevant sensor types.
The group’s wiki and final report give a number of examples. Such as linked
open data examples from the SENSEI5 project [1], semantic annotation from
Kno.e.sis,6 a SmartProducts7 example and sensor datasheets.
5
http://www.sensei-project.eu/
6
http://knoesis.wright.edu/
7
http://www.smartproducts-project.eu/
Fig. 4. Simplified view of systems, deployments, platforms, and operating and survival
conditions. Sensors, along with other things, maybe systems.
Additionally, the SSN ontology is used in the SemsorGrid4Env project,8 the
SPITFIRE project,9 the EXALTED project,10 at 52north11 and at CSIRO.12
It was also used in publishing linked data from the Spanish Meteorological
Agency.13 Known uses and papers were listed at
http://www.w3.org/2005/Incubator/ssn/wiki/Tagged_Bibliography.
2 Future Directions
In developing the ontology, the group worked to include only the sensor specific
concepts and properties, thus the need to include domain and other concerns
when using the ontology. However, concepts from the systems perspective (Sys-
tem, Deployment, Platform, etc.) arent extensions of the SSO pattern, and this
leads naturally to questioning their place in the ontology.
Clearly the system perspective is often needed, so it’s natural to have in-
cluded it, but these concepts aren’t sensor only. Similarly, time series and other
concepts not in the ontology are often used, but not sensor only. This suggests
a more modular structure, in which the central enabler (the sensor ontology) is
as simple as possible and other frequently used concepts are provided in small
‘stem’ modules. This wouldn’t facilitate further capability, but it does clean up
the ontology and guide its use. The SSO pattern (8 concepts) would be the
8
http://www.semsorgrid4env.eu/
9
http://www.spitfire-project.eu/
10
http://www.ict-exalted.eu/
11
52north.org/
12
http://www.csiro.au/science/Sensors-and-network-technologies.html
13
http://aemet.linkeddata.es/
starting module, with the remaining sensor only concepts (largely measurement
capabilities) in another module (14 concepts), then systems, timeseries and the
like in small largely independent modules.
An open community, formed around the ontology and semantic sensor net-
works in general, could maintain the ontology as well as document use, examples
and common patterns.
As for further use of the ontology, it’s likely to at least be used further in
CSIRO sensor and provenance projects, at 52 North and Kno.e.sis, in the SPIT-
FIRE project and internet of things projects. The array of applications in which
the ontology could enable includes provenance and decision making, scientific
processing and reasoning, streaming data, and other management, querying and
reasoning tasks. Internet of things applications also invite the option of linking
sensing to actuation.
Ideally, manufacturers would provide machine-readable specifications of their
sensor datasheets, using the SSN ontology.
3 Acknowledgements
An article [4], covering, in greater depth, the ontology, the group and the exam-
ples, will be available soon.
Without the time and effort of all the members of the incubator group the
construction of the ontology and its many uses would not have been possible.
The following are credited in the group’s final report and upcoming article.
Payam Barnaghi, Luis Bermudez, Raúl Garcı́a-Castro, Oscar Corcho,
Simon Cox, John Graybeal, Manfred Hauswirth, Cory Henson, Arthur
Herzog, Vincent Huang, Krzysztof Janowicz, W. David Kelsey, Danh Le
Phuoc, Laurent Lefort (Chair), Myriam Leggieri, Victor Manuel Pelaez
Martinez, Holger Neuhaus (Former Chair), Andriy Nikolov, Kevin Page,
Amit Parashar (Former Chair), Alexandre Passant, Amit Sheth (Chair)
and Kerry Taylor (Chair).
References
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shop on Semantic Sensor Networks (2010)
2. Botts, M., Robin, A.: OpenGIS Sensor Model Language (SensorML) implementa-
tion specification. OpenGIS Implementation Specification OGC 07-000, The Open
Geospatical Consortium (July 2007)
3. Cameron, M., Wu, J., Taylor, K., Ratcliffe, D., Squire, G., Colton, J.: Semantic so-
lutions for integration of ocean observations. In: 2nd International Semantic Sensor
Networks Workshop (2009)
4. Compton, M., Barnaghi, P., Bermudez, L., Garcı́a-Castro, R., Corcho, O., Cox, S.,
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