G. Cassar Semantic IoT Semantic Inter-Operability Practices-Part1 presented at the IERC AC4 IoT Semantic Interoperability workshop, Guildford, UK, 15 April 2013

1. IoT Semantic Inter-Operability Event
Part 1: IoT semantic interoperability practices
Presenter: Gilbert Cassar
Centre for Communication Systems Research, University of Surrey
Contributors: Dr. Wei Wang, Dr. Payam Barnaghi, Dr. Martin Serrano,
Mr. Phillippe Cousin

2. Getting Started
 Install Virtual Box
 Copy ‘InteropEventVM’ from the USB sticks
provided.
 Load the VM on Virtual Box.
 Also on the USB Stick:
 Sensor Ontologies
 Quantity Type ontologies.

3. Getting started with Protégé 4
 Protégé is an OWL-specific integrated
development environment (IDE) for developing and
maintaining OWL ontologies.
 Already installed on your VM.
 To start Protégé:
Home/protégé_4.2/run.sh

4. Getting Started with Protégé 4
 Tutorials for Protégé 4.2 can be found at:
 http://protegewiki.stanford.edu/wiki/Protege4GettingStarted
 http://protegewiki.stanford.edu/wiki/Protege4Pizzas10Minutes
 Creating OWL ontologies:
 Open existing OWL ontologies
 Open an ontology at a URL
 Import existing ontologies
 Each ontology should have a unique default
namespace.

5. Creating classes
 Named classes - create a class and assign a name
to it. Two ‘built in’ named classes: owl:Thing and
owl:Nothing.
 Defining subclass: rdfs:subClassof
 Asserting a class is the same as another:
owl:equivalentClass
 Asserting a class is disjoint with another:
owl:disjointWith
http://protege.stanford.edu/conference/2005/slides/T2_OWLTutorialI_Drummond_final.pdf

6. Checking ontologies
 We would like to automatically check our ontology
to ensure that the logical meaning corresponds to
the intended meaning, e.g., an individual of a class
shouldn’t be an individual of its disjoint classes.
 For an ontology that falls into the scope of OWL-
DL, we can use a DL Reasoner to infer information
that isn’t explicitly represented in the ontology.
http://protege.stanford.edu/conference/2005/slides/T2_OWLTutorialI_Drummond_final.pd
f

7. Reasoning in Protégé
 DL reasoner can be plugged into Protégé
 HermiT
 Fact++
 Standard reasoning services:
 Subsumption checking
 Equivalence checking
 Consistency checking
 Instantiation checking

8. Creating properties
 OWL has two main types of properties:
 Object properties
 Datatype properties.
 Object properties relate an individual to an
individual.
 Datatype properties link an individual to a data
value.
 Annotation properties can be used to attach ‘meta-
data’ to classes, properties and individuals.
http://protege.stanford.edu/conference/2005/slides/T2_OWLTutorialI_Drummond_final.pd
f

9. More on properties
 OWL supports the specification of a property
hierarchy; in OWL-DL, object properties may only
have object properties as super-properties, and
same for datatype properties.
 Properties have a Domain and a Range.
http://protege.stanford.edu/conference/2005/slides/T2_OWLTutorialI_Drummond_final.pd
f

10. Exercises 1: use Protégé
 Study the following ontologies in Protégé:
 W3C SSN: http://purl.oclc.org/NET/ssnx/ssn
 OWL-S:
 http://www.daml.org/services/owl-s/1.2/Service.owl
 http://www.daml.org/services/owl-s/1.2/Process.owl
 http://www.daml.org/services/owl-s/1.2/Profile.owl
 http://www.daml.org/services/owl-s/1.2/Grounding.owl
 IoT-A ontologies:
 http://personal.ee.surrey.ac.uk/Personal/P.Barnaghi/ontology/EntityModel.owl
 http://personal.ee.surrey.ac.uk/Personal/P.Barnaghi/ontology/ResourceModel.owl
 http://personal.ee.surrey.ac.uk/Personal/P.Barnaghi/ontology/ServiceModel.owl

11. Exercises 1: use Protégé cont’d
 Open the following ontologies in Protégé:
 IoT.est ontologies:
 http://ict-iotest.eu/iotest/ontologies/v1.0/IoT.est-Resource.owl
 http://ict-iotest.eu/iotest/ontologies/v1.0/IoT.est-Service.owl
 http://ict-iotest.eu/iotest/ontologies/v1.0/IoT.est-Test.owl
 http://ict-iotest.eu/iotest/ontologies/v1.0/IoT.est-QoSQoI.owl

12. What is expected from the semantic
interoperability?
 Unified access to data:
 unified descriptions and at the same time an open
framework.
 Self-descriptive data and re-usable knowledge.
 Deriving additional knowledge.
 Reasoning support and association to other entities
and resources.
 Enabling autonomous interactions with the resources.

13. Potential solutions
 Using machine-readable and machine-interpretable
meta-data
 Well defined standards and description frameworks: XML,RDF, OWL,
etc.
 Variety of technologies and tools for creating/managing/querying and
accessing semantic data, e.g., Jena, Sesame, Protége, etc.
 Ontologies defines conceptualisation of a domain.
 Domain concepts modeling
 Relationships between the concepts
 Link to existing knowledge, the linked open data cloud

14. Semantics in IoT – myth and reality
 #1: If we create an Ontology our data is
interoperable
 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 don’t understand it but can
interpret it. It still does need intelligent processing, reasoning mechanism
to process and interpret the data.

15. Semantics in IoT – myth and reality
 #3: It’s a Hype! Ontologies and semantic data are
too much overhead; we deal with tiny devices in IoT.
 Reality: Ontologies are a way to share and agree on a common vocabulary
and knowledge; at the same time there are machine-interpretable and
represented in interoperable and re-usable forms;
 You don’t necessarily need to add semantic metadata in the source- it could be
added 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.

16. Exercises 2: create an ontology for IoT
 Considering reuse of the existing ontologies (using
‘import’ in Protégé)
 Consider the following concepts in the IoT domain:
 Resource (sensor, actuator, RFID)
 Other resources (gateway, directory, server)
 Service (related to IoT resources; as well as service
lifecycle related information)
 Systems, subsystems
 Observation and measurement
 Relationships among the concepts
 Link to existing knowledge (location)

17. Ontology matching for improving
interoperability
 Also known as ontology alignment or ontology
mapping.
 Formally, is the process of determining
correspondences between semantically related
entities from (two) ontologies. A set of
correspondences is also called an alignment.
 Can be used to support various tasks
 Ontology merging
 Assisting ontology engineering for humans

18. A simplified ontology matching task
 Two ontologies: Os (source) and Od (destination)
 To establish correspondence between two concepts
Cs from Os and Cd from Od:
 Check equivalence for classes and relations
 Check similarity if equivalence cannot be confirmed
 A similarity or confidence value is calculated using some mechanisms
 No matching
 Produce report: equivalence, similarity, and those concepts which cannot
be matched
 This will help us in the ontology engineering process.

19. Matching algorithm based on lexical and
structural information
 Two classes are equivalent if:
 Their URIs are same
 They are both equivalent to a third class
 If no equivalent relation found between two classes,
then we try to find out if two classes have
relatedness:
 subclass/superclass/subproperty/superproperty
 sibling
 have Common Ancester
 lexically similar: check two classes’ labels (e.g., edited distance
algorithm)

20. Pseudo-code
For 0<i<m (vector c1)
For 0<j<n (vector c2)
if cheEuqivalence(c1[i], c2[j]) assert equivalent;
else
if checkRelatedness(c1[i], c2[j]) assert
checkRelationType (c1[i], c2[j]);
End if
End if
End For
End for

21. Exercise 3: Check the interoperability of
your model against existing ones.
 Ontology matching tool:
 http://localhost:8080/InteropOntologyCheckingTool/
 http://iotserver3.ee.surrey.ac.uk:8080/InteropOntologyMatchingTool/
 http://ccsriottb3.ee.surrey.ac.uk:8080/InteropOntologyMatchingTool/
 Input ontologies:
 The IoT ontology developed in exercise 2
 The existing ontologies for sensors (SSN), services (OWL-S)
and IoT (IoT-A, IoT.est)
 Discussion:
 How similar to existing models is your model?

22. Thank you

23. Linked Open Data
~ 50 Billion Statements

24. Linked Data as an independent layer
in the Internet architecture
Images from Stefan Decker, http://fi-ghent.fi-week.eu/files/2010/10/Linked-Data-scheme1.png; linked data diagram: http://richard.cyganiak.de/2007/10/lod/

25. Linked data and interoperability
 Linked Data is becoming an accepted best practice
to exchange information in an interoperable and
reusable fashion.
 Many different communities on the Internet use
Linked Data standards to provide and exchange
interoperable information.
 We have seen methods mainly for improving
interoperability at ontology (schema) level, now we
look at interoperability at data level.
http://linkeddata.future-internet.eu/index.php/Main_Page

26. Building interoperability
 Metadata standards:
 Dublin core, FOAF, SSN and IoT.est (domain specific)
 Existing vocabularies:
 NCI, SSN-QU
 Other knowledge base and ontologies
 DBPedia, Geonames
 Relationships:
 SKOS closeMatch, exactMatch, broadMatch,
narrowMatch, relatedMatch
 owl:sameAs, rdf:seeAlso

27. Linked Data and interoperability
based on links
 “The Web of data proposes a style of
interoperability which doesn't rely on synchronous
query of separate databases, nor on reducing
databases into a common format, but on the
creation of a global information space, using links to
browse seamlessly between resources.”
Emmanuelle Bermes, "Convergence and Interoperability: a Linked Data perspective"

28. Linked data principles
 using URI’s as names for things: Everything is
addressed using unique URI’s.
 using HTTP URI’s to enable people to look up those
names: All the URI’s are accessible via HTTP
interfaces.
 provide useful RDF information related to URI’s
that are looked up by machine or people;
 including RDF statements that link to other URI’s to
enable discovery of other related concepts of the
Web of Data: The URI’s are linked to other URI’s.

29. Linked data in IoT
 Using URI’s as names for things;
- URI’s for naming IoT resources and data (and also streaming channels and
data);
 Using HTTP URI’s to enable people to look up those names;
- Web-level access to low level sensor data and real world resource descriptions
(gateway and middleware solutions);
 Providing useful RDF information related to URI’s that are
looked up by machine or people;
- publishing semantically enriched resource and data description: temporal,
spatial, thematic;
 Including RDF statements that link to other URI’s to enable
discovery of other related things of the web of data;
- linking and associating the real world data to the existing data on the Web;

30. Creating and using linked sensor data
http://ccsriottb3.ee.surrey.ac.uk:8080/IOTA/

31. Sensor discovery using linked sensor data