Semantic IoT Semantic Inter-Operability Practices - Part 2
26
IoT Semantic Inter-Operability Event Part 2: IoT semantic interoperability practices Presenter: Gilbert Cassar Centre for Communication Systems Research, University of Surrey Contributors: Dr. Payam Barnaghi, Dr. Martin Serrano, Mr. Phillippe Cousin
Semantic IoT Semantic Inter-Operability Practices - Part 2
G. Cassar Semantic IoT Semantic Inter-Operability Practices-Part2 presented at the IERC AC4 IoT Semantic Interoperability workshop, Guildford, UK, 15 April 2013
Citation preview
1. IoT Semantic Inter-Operability EventPart 2: IoT semantic
interoperability practicesPresenter: Gilbert CassarCentre for
Communication Systems Research, University of SurreyContributors:
Dr. Payam Barnaghi, Dr. Martin Serrano, Mr. Phillippe Cousin
2. People want answers, not numbers(Steven Glaser, UC
Berkley)Sinknode GatewayCore networke.g. InternetWhat is the
temperature at home?Freezing!
3. Turning Data into WisdomDataInformationKnowledgeWisdomRaw
sensory dataStructured data (withsemantics)Abstraction and
perceptionsActionable intelligence
4. Components Related to Things Physical world objects e.g. A
room, a car, A person; Feature of Interest e.g. Temperature of the
room, Location of the car, heart-rate of the person; Sensors e.g.
Temperature sensor, GPS, pulse sensor
5. How to say what a Sensor is andwhat it
measuresSinknodeGateway
6. Semantics and IoT Data Creating ontologies and defining data
models is not enough tools to create and annotate data data
handling components Complex models and ontologies look good, but
design lightweight versions for constrained environments think of
practical issues make it as compatible as possible and/or link it
to the other existing ontologies Domain knowledge and instances
Common terms and vocabularies Location, unit of measurement, type,
theme, Link it to other resources Linked-data URIs and naming
7. 7Semantics and Linked-data The principles in designing the
linked data aredefined as: using URIs as names for things; using
HTTP URIs to enable people to look up thosenames; provide useful
RDF information related to URIs that arelooked up by machine or
people; including RDF statements that link to other URIs toenable
discovery of other related concepts of the Webof Data;
8. 8Linked Sensor data
9. 9Myth and reality #1: If we create an Ontology our data
isinteroperable Reality: there are/could be a number of ontologies
for a domain Ontology mapping Reference ontologies Standardisation
efforts #2: Semantic data will make my data machine-understandable
and my system will be intelligent. Reality: it is still meta-data,
machines dont understand it but caninterpret it. It still does need
intelligent processing, reasoning mechanismto process and interpret
the data.
10. 10Myth and reality #3: Its a Hype! Ontologies and semantic
data aretoo much overhead; we deal with tiny devices in IoT.
Reality: Ontologies are a way to share and agree on a common
vocabularyand knowledge; at the same time there are
machine-interpretable andrepresented in interoperable and re-usable
forms; You dont necessarily need to add semantic metadata in the
source- it could beadded to the data at a later stage (e.g. in a
gateway); Legacy applications can ignore it or to be extended to
work with it.
11. The Importance of Domain Knowledge Created with the help of
domain experts. Provides a common understanding of the domain
forpeople and machines to refer to. Allows machines to determine
the relationshipbetween assertions coming from the same domain.
Whats the relationship between temperature and weather? Easier to
provide suggestions to engineers building asemantic description of
their sensor.
12. Exercises 1 Open the following ontologies in Protg:
Quantity and Dimensions ontologies:
http://purl.oclc.org/NET/ssnx/qu/qu
http://purl.oclc.org/NET/ssnx/qu/qu-rec20 Units ontology:
http://localhost:8080/InteropOntologyMatchingTool/Ontos/Units.owl
http://qudt.org/1.1/schema/dimension
14. Input and Output Parameters A very important part of any
semanticallyannotated service description. Used by:Discovery
Engines.Semantic Matchmakers.Composition Engines.Compensation
Engines.
15. Importance of Service Parameters
16. Describing Service Parameters
17. Filters Used By Semantic Matchmakers Where A and B are
parametertypes.The Subsumes filter is less usefulthan the other two
because when Ais more generic than B, A cannotinteroperate with B
in most cases.
18. QU-rec20 Ontology Ontology for Quantity Kinds and Units:
units andquantities definitions This ontology imports the qu
ontology derived fromthe work done by the SysML 1.2 QUDV
workinggroup (see http://purl.oclc.org/NET/ssnx/qu/qu fordetails).
Defines a huge variety of dimensions and could beused a common
domain for describing the type ofdata measured by a sensor.
19. QUDT Ontology Ontology for Quantities, Units, Dimensions
and DataTypes. Developed by TopQuadrant and NASA. Another
standardisation effort. Compare with theQU-rec20 ontology.
20. QoS/QoI Ontology Created as part of the IoT.est
Projecthttp://ict-iotest.eu/iotest/ Contains various definitions
for Quality ofService and Quality of Informationattributes that
could be used to describe aservice parameter.
21. Useful Domain Ontologies Quantity and Dimensions
ontologies: http://purl.oclc.org/NET/ssnx/qu/qu
http://purl.oclc.org/NET/ssnx/qu/qu-rec20 Units ontology:
http://localhost:8080/InteropOntologyMatchingTool/Ontos/Units.owl
http://qudt.org/1.1/schema/dimension
23. Exercises 2: create a parameter ontology Considering reuse
of the existing ontologies (usingimport in Protg) Consider the
following parameter attributes: Data Type Unit of Measure Response
Time Location More information also means more overhead.
24. Exercise 3: Comparing your parametermodel with others Copy
your parameter description on a usb stick. Transfer it to the
Virtual Machine of another person sat at yourtable. Save it in the
folder: Home/apache/apache-tomcat-6.0.36/webapps/docs/ontology/ The
URL of your model should now be:
http://localhost:8080/InteropOntologyCheckingTool/docs/ontology/yourontology.owl
Use the Interoperability tool at:
http://localhost:8080/InteropOntologyCheckingTool/ Compare your
parameter model to the other persons modelto check how
interoperable they are.
25. Exercise 3: Discussion How interoperable is your model with
otherpeoples model? Have you re-used existing structures (for
examplefrom the IoT.est service model) ?