Lesson from María Poveda on "Ontologies for Smart Cities" in the 1st Summer School on Smart Cities and Linked Open Data (LD4SC 2015).

1. LD4SC
Summer
School
7th
-­‐
12th
June,
Cercedilla,
Spain
LD4SC
Summer
School
7th
-­‐
12th
June,
Cercedilla,
Spain
Ontologies
for
Smart
Ci?es
Oscar
Corcho,
María
Poveda
Villalón,
Asunción
Gómez
Pérez,
Filip
Radulovic,
Raúl
García
Castro
UPM

2. LD4SC
Summer
School
7th
-­‐
12th
June,
Cercedilla,
Spain
LD4SC
Summer
School
7th
-­‐
12th
June,
Cercedilla,
Spain
What
is
an
ontology?
We
may
also
call
them
“vocabularies”,
“shared
informa?on
models”
or
“shared
data
structures”

3. LD4SC
Summer
School
7th
-­‐
12th
June,
Cercedilla,
Spain
Ontologies
• What
is
an
Ontology
– “An
ontology
is
a
formal,
explicit
specifica9on
of
a
shared
conceptualiza9on”.
[Studer,
Benjamins,
Fensel.
Knowledge
Engineering:
Principles
and
Methods.
Data
and
Knowledge
Engineering.
25
(1998)
161-­‐197]
• Components
• Types:
– Lightweight/heavyweight
– Applica?on/Domain/General
• What
are
they
for
– Describe
a
domain
– Data
integra?on
– Reasoning
– …
• Languages:
– OWL
Web
Ontology
Language,
RDF
Schema
Ontology
Instances
Knowledge
Level
Data Level
Concepts
Taxonomies
Relations
Attributes
Axioms
Instances of concepts
Instances of relations
Instances of attributes

4. LD4SC
Summer
School
7th
-­‐
12th
June,
Cercedilla,
Spain
Mo?va?on
4
Create
terms
(if
needed)
Put
them
all
together
4
“Linking
Open
Data
cloud
diagram,
by
Richard
Cyganiak
and
Anja
Jentzsch.
hep://lod-­‐cloud.net/”
My
Data
Set
My
namespace
Vocabulary
describing
my
data
Generate
RDF
Publish
my
DataSet
Reuse
terms
from
LOD
cloud
ºC
kWh
mt
F
K
m3
Developing Ontologies for Representing Data about Key Performance Indicators – María Poveda Villalón

5. LD4SC
Summer
School
7th
-­‐
12th
June,
Cercedilla,
Spain
LD4SC
Summer
School
7th
-­‐
12th
June,
Cercedilla,
Spain
How
to
develop
an
ontology?

6. LD4SC
Summer
School
7th
-­‐
12th
June,
Cercedilla,
Spain
What is Ontological Engineering?
It refers to the set of activities that
concern:
• the ontology development
process,
• the ontology life cycle,
• the methods and
methodologies for building
ontologies,
• the tools and tool suites
• and the languages that support
them

7. LD4SC
Summer
School
7th
-­‐
12th
June,
Cercedilla,
Spain
Ontology
development
[1]
Suárez-­‐Figueroa,
M.C.
PhD
Thesis:
NeOn
Methodology
for
Building
Ontology
Networks:
SpecificaAon,
Scheduling
and
Reuse.
Spain.
June
2010.
Activity definition taken from [1]
6. Ontology
implementation
5. Ontology selection
1. Requirements definition
Can you
represent all
your data?
7. Ontology evaluation
2. Terms extraction
3. Ontology conceptualization
4. Ontology search
6.2 Ontology
completion
3.1 Initial model drafting
3.2 Detailed model definition
6.1 Ontology integration
Focus of each activity
Existing tools to carry out the activity
Tips, alternatives and references
7

8. LD4SC
Summer
School
7th
-­‐
12th
June,
Cercedilla,
Spain
1.
Requirements
defini?on
Ontology Requirements: refers to the activity of collecting the
requirements that the ontology should fulfil (for example, reasons to build
the ontology, identification of target groups and intended uses). (NeOn)
6. Ontology
implementation
5. Ontology selection
1. Requirements definition
Can you
represent all
your data?
7. Ontology evaluation
2. Terms extraction
3. Ontology conceptualization
4. Ontology search
6.2 Ontology
completion
3.1 Initial model drafting
3.2 Detailed model definition
6.1 Ontology integration
8
Proposed references:
- NeOn Guidelines for non functional
requirements.
- Competency Questions technique [1]
Tools:
mind
map,
text
editor,
etc
[1]
Gruninger,
M.,
Fox,
M.
S.
The
role
of
competency
quesAons
in
enterprise
engineering.
In
Proceedings
of
the
IFIP
WG5.7
Workshop
on
Benchmarking
-­‐
Theory
and
Prac?ce,
Trondheim,
Norway,
1994.

9. LD4SC
Summer
School
7th
-­‐
12th
June,
Cercedilla,
Spain
Ontology
development
–
LCC
example
6. Ontology
implementation
5. Ontology selection
1. Requirements definition
Can you
represent all
your data?
7. Ontology evaluation
2. Terms extraction
3. Ontology conceptualization
4. Ontology search
6.2 Ontology
completion
3.1 Initial model drafting
3.2 Detailed model definition
6.1 Ontology integration
LCC
example
(Data
from
Leeds
City
Council
energy
consump?on)
Non
func?onal
requirements
specified:
• The
ontology
will
try
to
adopt
concepts
and
design
paeerns
in
other
ontologies
where
possible
• The
ontology
should
be
implemented
in
OWL
2
DL
9
Func?onal
requirements
(Competency
Ques9ons):
• What
was
the
average
electricity
consump?on
in
2014
by
district
in
Leeds?

10. LD4SC
Summer
School
7th
-­‐
12th
June,
Cercedilla,
Spain
2.
Terms
extrac?on
Ontology term extraction to extract a glossary of terms that
may be developed.
Tools for terminology extraction:
• Identify nouns, verbs, etc.
• Tools: Freeling for free text
6. Ontology
implementation
5. Ontology selection
1. Requirements definition
Can you
represent all
your data?
7. Ontology evaluation
2. Terms extraction
3. Ontology conceptualization
4. Ontology search
6.2 Ontology
completion
3.1 Initial model drafting
3.2 Detailed model definition
6.1 Ontology integration
Focus:
• Extract terminology from Competency Questions (NeOn)
• Extract terminology directly from the data
• Expert advise || Done by experts
10
Complete the list with synonyms

11. LD4SC
Summer
School
7th
-­‐
12th
June,
Cercedilla,
Spain
Ontology
development
–
LCC
example
6. Ontology
implementation
5. Ontology selection
1. Requirements definition
Can you
represent all
your data?
7. Ontology evaluation
2. Terms extraction
3. Ontology conceptualization
4. Ontology search
6.2 Ontology
completion
3.1 Initial model drafting
3.2 Detailed model definition
6.1 Ontology integration
Site
place
Address
PostCode
Electricity
Consumption, u?liza?on
years
time
11
What
was
the
average
electricity
consump?on
in
2014
by
district
in
Leeds?

12. LD4SC
Summer
School
7th
-­‐
12th
June,
Cercedilla,
Spain
3.
Ontology
conceptualiza?on
Ontology conceptualization refers to the activity of
organizing and structuring the information (data, knowledge,
etc.), obtained during the acquisition process, into meaningful
models at the knowledge level and according to the ontology
requirements specification document. (NeOn)
6. Ontology
implementation
5. Ontology selection
1. Requirements definition
Can you
represent all
your data?
7. Ontology evaluation
2. Terms extraction
3. Ontology conceptualization
4. Ontology search
6.2 Ontology
completion
3.1 Initial model drafting
3.2 Detailed model definition
6.1 Ontology integration Drawing tools, including paper and pencil
Focus drafting (optional):
• Identify main domains and top concept
• Establish relations between concepts and domains
Focus detail model:
• Establish hierarchies
• Establish specific relationships among defined
elements, rules, axioms, etc.
12
Do not try to define everything. You might
change your mind during the implementation.

13. LD4SC
Summer
School
7th
-­‐
12th
June,
Cercedilla,
Spain
Ontology
development
–
LCC
example
6. Ontology
implementation
5. Ontology selection
1. Requirements definition
Can you
represent all
your data?
7. Ontology evaluation
2. Terms extraction
3. Ontology conceptualization
4. Ontology search
6.2 Ontology
completion
3.1 Initial model drafting
3.2 Detailed model definition
6.1 Ontology integration
Council(
site(
Consump.on(
related(to(council(site(
Time(
has(.me(period(
Council(site
Has(address(
Address
Has(value(
Observa4on
Value
SensorOutput
Observa4on(result(
About(council(
Council(
site(
Consump4on(
In(city(
City
District
Is(in(district(
Place
Is(a(
13

14. LD4SC
Summer
School
7th
-­‐
12th
June,
Cercedilla,
Spain
4.
Ontology
search
Ontology search refers to the activity of finding candidate
ontologies or ontology modules to be reused (NeOn).
6. Ontology
implementation
5. Ontology selection
1. Requirements definition
Can you
represent all
your data?
7. Ontology evaluation
2. Terms extraction
3. Ontology conceptualization
4. Ontology search
6.2 Ontology
completion
3.1 Initial model drafting
3.2 Detailed model definition
6.1 Ontology integration
Search tools:
• General purpose:
• LOV: http://lov.okfn.org
• Google, Swoogle, Watson
• Others: ODP Portal http://ontologydesignpatterns.org
• Domain base:
• Smart cities: http://smartcity.linkeddata.es/
Focus:
• Terms already used in LOD
• Save time and resources
• Increase interoperability
Use domain terms and synonyms
Do not spend too much
time trying to find terms
for everything. You might
need to create them.
14

15. LD4SC
Summer
School
7th
-­‐
12th
June,
Cercedilla,
Spain
Ontology
development
–
LCC
example
6. Ontology
implementation
5. Ontology selection
1. Requirements definition
Can you
represent all
your data?
7. Ontology evaluation
2. Terms extraction
3. Ontology conceptualization
4. Ontology search
6.2 Ontology
completion
3.1 Initial model drafting
3.2 Detailed model definition
6.1 Ontology integration
15

16. LD4SC
Summer
School
7th
-­‐
12th
June,
Cercedilla,
Spain
5.
Ontology
selec?on
Ontology Selection refers to the activity of choosing the most suitable
ontologies or ontology modules among those available in an ontology
repository or library, for a concrete domain of interest and associated
tasks. (NeOn)
Evaluation tools:
• OOPS! – OntOlogy pitfalls scanner [1]
http://oops.linkeddata.es/
• Triple checker http://graphite.ecs.soton.ac.uk/checker/
(already included in OOPS!)
• Vapour http://validator.linkeddata.org/vapour (to be included
in OOPS!)
Also it should be considered:
• Modelling issues (OOPS!, reasoners, manually review, etc.)
• Domain coverage (based on the data to be represented)
• Used in Linked Data (LOD2Stats, Sindice, etc)
Focus:
• Assessment by Linked Data principles
• Modelling issues
• Domain coverage: data driven
[1]
Poveda-­‐Villalón,
M.,
Gómez-­‐Pérez,
A.,
&
Suárez-­‐Figueroa,
M.
C.
(2014).
Oops!(ontology
pitall
scanner!):
An
on-­‐line
tool
for
ontology
evalua?on.
InternaAonal
Journal
on
SemanAc
Web
and
InformaAon
Systems
(IJSWIS),
10(2),
7-­‐34.
6. Ontology
implementation
5. Ontology selection
1. Requirements definition
Can you
represent all
your data?
7. Ontology evaluation
2. Terms extraction
3. Ontology conceptualization
4. Ontology search
6.2 Ontology
completion
3.1 Initial model drafting
3.2 Detailed model definition
6.1 Ontology integration
Further reference:
NeOn Guidelines
16

17. LD4SC
Summer
School
7th
-­‐
12th
June,
Cercedilla,
Spain
Ontology
development
–
LCC
example
6. Ontology
implementation
5. Ontology selection
1. Requirements definition
Can you
represent all
your data?
7. Ontology evaluation
2. Terms extraction
3. Ontology conceptualization
4. Ontology search
6.2 Ontology
completion
3.1 Initial model drafting
3.2 Detailed model definition
6.1 Ontology integration
• Domain
coverage
• Schema.org
for
public
places
and
provides
some
addi?onal
terms
and
proper?es
that
can
be
used(e.g.,
PostalAddress
and
City)
• Also
widely-­‐known
and
accepted
vocabulary
à
interoperability
• Closer
seman9cs
•
ero:FinalEnergy
class
from
the
Energy
Resource
and
the
ssn:Property
class
from
the
SSN
ontology
in
order
to
represent
specific
indicator
for
which
the
consump?on
is
related
to
17

18. LD4SC
Summer
School
7th
-­‐
12th
June,
Cercedilla,
Spain
6.
Ontology
implementa?on.
Integra?on
Ontology Integration. It refers to the activity of including one ontology
in another ontology. (NeOn)
Tools:
• Ontology editors: Protégé, NeOn Toolkit, etc.
• Plug-ins: Ontology Module Extraction and Partition
• Text editors for manual approach
Focus:
• How much information should I reuse?
• How to reuse the elements or vocabs?
• Should I import another ontology?
• Should I reference other ontology element URIs?
• ... replicating manually the URI?
• ... merging ontologies?
• How to link them?
Techniques:
• Import the ontology as a whole
• Reuse some parts of the ontology (or ontology module)
• Reuse statements
6. Ontology
implementation
5. Ontology selection
1. Requirements definition
Can you
represent all
your data?
7. Ontology evaluation
2. Terms extraction
3. Ontology conceptualization
4. Ontology search
6.2 Ontology
completion
3.1 Initial model drafting
3.2 Detailed model definition
6.1 Ontology integration
18

19. LD4SC
Summer
School
7th
-­‐
12th
June,
Cercedilla,
Spain
6.
Ontology
implementa?on.
Extension
Ontology Enrichment It refers to the activity of extending an ontology with
new conceptual structures (e.g., concepts, roles and axioms). (NeOn)
Focus:
• How should I create terms according to ontological foundations
and Linked Data principles?
Ontology development:
• Ontology Development 101: A Guide to Creating Your First
Ontology [1]
• Ontology Engineering Patterns http://www.w3.org/2001/sw/
BestPractices/
• Extracting ontology conceptualization, formalization
techniques from existing methodologies
Recommendation
• Link to existing entities
• Provide human readable documentation
• Keep the semantics of the reused elements
[1]
Natalya
F.
Noy
and
Deborah
L.
McGuinness.
Ontology
Development
101:
A
Guide
to
CreaAng
Your
First
Ontology’.
Stanford
Knowledge
Systems
Laboratory
Technical
Report
KSL-­‐01-­‐05
and
Stanford
Medical
Informa?cs
Technical
Report
SMI-­‐2001-­‐0880,
March
2001.
Tools:
• Ontology editors: Protégé, NeOn Toolkit, etc.
6. Ontology
implementation
5. Ontology selection
1. Requirements definition
Can you
represent all
your data?
7. Ontology evaluation
2. Terms extraction
3. Ontology conceptualization
4. Ontology search
6.2 Ontology
completion
3.1 Initial model drafting
3.2 Detailed model definition
6.1 Ontology integration
19

20. LD4SC
Summer
School
7th
-­‐
12th
June,
Cercedilla,
Spain
time:Interval
schema:City
ssn:Observation
ssn:observation
SamplingTime
ssn:SensorOutput
ssn:ObservationValue
ssn:hasValue
ssn:FeatureOf
Interest
ssn:featureOf
Interest
lcc:hasQuantityValue :: xsd:decimal
ssn:Property
ero:FinalEnergy
ssn:observed
Property
ssn:observation
Result
Legend
Class
datatype property :: datatype
object property
subclass of relation
schema:CivicStructure
lcc:uprn :: xsd:String
dc:title :: xsd:String
schema:PostalAddress
schema:addressLocality :: xsd:String
schema:addressRegion :: xsd:String
schema:streetAddress :: xsd:String
schema:postalCode :: xsd:String
schema:address
admingeo:District
admingeo:district
time:Instant
time:inXSDDateTime :: xsd:dateTime
time:hasBeginning
time:hasEnd
ero:Energy
ConsumerFacility
ero:consumes
EnergyType
om:Unit_of_measure
lcc:hasQuantityUnitOf
Measurement
SupplyOrStorageSite
OpenAirSite
AccomodationSite AdministrativeSite
OfficeSite
EducationalSite
SocialSite
OtherSite
CulturalSite
schema:containedIn
schema:Place
schema:Administrative
AreaLeisureSite
Ontology
development
–
LCC
example
6. Ontology
implementation
5. Ontology selection
1. Requirements definition
Can you
represent all
your data?
7. Ontology evaluation
2. Terms extraction
3. Ontology conceptualization
4. Ontology search
6.2 Ontology
completion
3.1 Initial model drafting
3.2 Detailed model definition
6.1 Ontology integration
20

21. LD4SC
Summer
School
7th
-­‐
12th
June,
Cercedilla,
Spain
Ontology
evalua?on
Ontology Evaluation it refers to the activity of checking the
technical quality of an ontology against a frame of reference. (NeOn)
Evaluation tools related to Linked Data principles:
• OOPS! – OntOlogy pitfalls scanner [1]
http://oops.linkeddata.es/
• Triple checker http://graphite.ecs.soton.ac.uk/checker/
(already included in OOPS!)
Evaluation tools/techniques other aspects:
• Modelling issues (OOPS!, reasoners, manually review, etc.)
• Domain coverage (based on the data to be represented)
• Application based (queries)
• Syntax issues: validators
Focus:
• Assessment by Linked Data principles
• Modelling issues
• Domain coverage: data driven
[1]
Poveda-­‐Villalón,
M.,
Gómez-­‐Pérez,
A.,
&
Suárez-­‐Figueroa,
M.
C.
(2014).
Oops!(ontology
pitall
scanner!):
An
on-­‐line
tool
for
ontology
evalua?on.
InternaAonal
Journal
on
SemanAc
Web
and
InformaAon
Systems
(IJSWIS),
10(2),
7-­‐34.
6. Ontology
implementation
5. Ontology selection
1. Requirements definition
Can you
represent all
your data?
7. Ontology evaluation
2. Terms extraction
3. Ontology conceptualization
4. Ontology search
6.2 Ontology
completion
3.1 Initial model drafting
3.2 Detailed model definition
6.1 Ontology integration
21

22. LD4SC
Summer
School
7th
-­‐
12th
June,
Cercedilla,
Spain
Ontology
development
–
LCC
example
Minor, mostly
lack of
annotations
in reused
terms.
6. Ontology
implementation
5. Ontology selection
1. Requirements definition
Can you
represent all
your data?
7. Ontology evaluation
2. Terms extraction
3. Ontology conceptualization
4. Ontology search
6.2 Ontology
completion
3.1 Initial model drafting
3.2 Detailed model definition
6.1 Ontology integration
22
hep://oops.linkeddata.es/

23. LD4SC
Summer
School
7th
-­‐
12th
June,
Cercedilla,
Spain
LD4SC
Summer
School
7th
-­‐
12th
June,
Cercedilla,
Spain
OWL
Web
Ontology
Language

24. LD4SC
Summer
School
7th
-­‐
12th
June,
Cercedilla,
Spain
Approaches
for
building
ontologies
UML
Frames & Logic
Subclass of
Mammal…
Subclass of
Birds
Subclass of
Subclass of Subclass of
Design time
Dog Cat
Description logic
Mammal
….
….
dogBirds
Cat
Automatic Classification
E/R Model

25. LD4SC
Summer
School
7th
-­‐
12th
June,
Cercedilla,
Spain
Lassila
and
McGuiness
Classifica?on
(I)
Catalog/ID
Thessauri
“narrower term”
relation
Formal
is-a
Frames
(properties)
General
Logical
constraints
Terms/
glossary
Informal
is-a
Formal
instance
Value
Restrs.
Disjointness,
Inverse, part-
Of ...
Lassila O, McGuiness D. The Role of Frame-Based Representation on the Semantic Web.
Technical Report. Knowledge Systems Laboratory. Stanford University. KSL-01-02. 2001.

26. LD4SC
Summer
School
7th
-­‐
12th
June,
Cercedilla,
Spain
Lassila
and
McGuiness
Classifica?on
(II)
Catalog/ID ThesaurusGlossary Informal is-a
Informal is-a
Types of relationships
Thesaurus
Catalog/ID
Informal is-a

27. LD4SC
Summer
School
7th
-­‐
12th
June,
Cercedilla,
Spain
Lassila
and
McGuiness
Classifica?on
(III)
Formal is-a Frames (properties) General
Logical
constraints
Formal instance Value
Restrs.
Disjointness,
Inverse, part-
Of ...Formal is-a
with
properties
(define-relation connects (?edge ?source ?target)
"This relation links a source and a target by an edge. The
source and destination are considered as spatial points. The
relation has the following properties: symmetry and irreflexivity."
:def (and (SpatialPoint ?source)
(SpatialPoint ?target)
(Edge ?edge))
:axiom-def
((=> (connects ?edge ?source ?target)
(connects ?edge ?target ?source)) ;symmetry
(=> (connects ?edge ?source ?target)
(not (or (part-of ?source ?target) ;irreflexivity
(part-of ?target ?source))))))
General
Logical
constraints
(define-class AmericanAirlinesFlight (?X)
:def (Flight ?X)
:axiom-def
(Disjoint-Decomposition AmericanAirlinesFlight
(Setof AA7462 AA2010 AA0488)))
(define-class Location (?X)
:axiom-def
(Partition Location
(Setof EuropeanLocation NorthAmericanLocation
SouthAmericanLocation AsianLocation
AfricanLocation AustralianLocation
AntarcticLocation)))
Disjointness
(define-class Travel (?travel)
"A journey from place to place"
:axiom-def
(and (Superclass-Of Travel Flight)
(Template-Facet-Value Cardinality
arrivalDate Travel 1)
(Template-Facet-Value Cardinality
departureDate Travel 1)
(Template-Facet-Value Maximum-Cardinality
singleFare Travel 1))
:def
(and (arrivalDate ?travel Date)
(departureDate ?travel Date)
(singleFare ?travel Number)
(companyName ?travel String)))
Value
Restrs.

28. LD4SC
Summer
School
7th
-­‐
12th
June,
Cercedilla,
Spain
OWL and Description Logics
• Automatic classification, done by the
inference engine, at run-time
Living Being
Invertebrate
Vertebrate
Dog
Plant
Cat
Automatic
Classification
Subclass of
Living Being
VertebrateInvertebrate
Subclass of
Plant
Subclass of
Subclass of Subclass of
Design time
Dog Cat

29. LD4SC
Summer
School
7th
-­‐
12th
June,
Cercedilla,
Spain
What is Description Logic?
• A family of logic-based Knowledge Representation formalisms
– Descendants of semantic networks and KL-ONE
– Describe domain in terms of concepts (classes), roles (relationships) and
individuals
• Specific languages characterised by the constructors and axioms used to assert
knowledge about classes, roles and individuals.
• Example: ALC (the least expressive language in DL that is propositionally closed)
– Constructors: boolean (and, or, not)
– Role restrictions
• Distinguished by:
– Formal semantics (model theoretic)
– Decidable fragments of FOL
– Provision of sound, complete and optimised inference services

30. LD4SC
Summer
School
7th
-­‐
12th
June,
Cercedilla,
Spain
Structure of DL Ontologies
• A DL ontology can be divided into two parts:
– Tbox (Terminological KB): a set of axioms that describe the structure of
a domain :
• Doctor ⊆ Person
• Person ⊆ Man ∪ Woman
• HappyFather ⊆ Man ∩ ∀hasDescendant.(Doctor ∪ ∀hasDescendant.Doctor)
– Abox (Assertional KB): a set of axioms that describe a specific situation :
• John ∈ HappyFather
• hasDescendant (John, Mary)
– Other terms that have been used:
• RBox
• EBox (extensional box)

31. LD4SC
Summer
School
7th
-­‐
12th
June,
Cercedilla,
Spain
DL constructors
≥3
hasChild,
≤1
hasMother
{Colombia,
Argen?na,
México,
...}
à
MercoSur
countries
hasChild-­‐
(hasParent)
≤2
hasChild.Female,
≥1
hasParent.Male
Other:
Concrete
datatypes:
hasAge.(<21)
Transi?ve
roles:
hasChild*
(descendant)
Role
composi?on:
hasParent
o
hasBrother
(uncle)

32. LD4SC
Summer
School
7th
-­‐
12th
June,
Cercedilla,
Spain
Most common constructors in class
definitions
• Intersection: C1 ∩ ... ∩ Cn Human ∩ Male
• Union: C1 ∪ ... ∪ Cn Doctor ∪ Lawyer
• Negation: ¬C ¬Male
• Nominals: {x1} ∪ ... ∪ {xn} {john} ∪ ... ∪ {mary}
• Universal restriction: ∀P.C ∀hasChild.Doctor
• Existential restriction: ∃P.C ∃hasChild.Lawyer
• Maximum cardinality: ≤nP ≤3hasChild
• Minimum cardinality: ≥nP ≥1hasChild
• Specific Value: ∃P.{x} ∃hasColleague.{Matthew}
• Nesting of constructors can be arbitrarily complex
– Person ∩ ∀hasChild.(Doctor ∪ ∃hasChild.Doctor)
• Lots of redundancy
– A∪B is equivalent to ¬(¬ A ∩ ¬B)
– ∃P.C is equivalent to ¬∀P. ¬C

33. LD4SC
Summer
School
7th
-­‐
12th
June,
Cercedilla,
Spain
Most common axioms
• Classes
– Subclass C1 ⊆ C2 Human ⊆ Animal ∩ Biped
– Equivalence C1 ≡ C2 Man ≡ Human ∩ Male
– Disjointness C1 ∩ C2 ⊆ ⊥ Male ∩ Female ⊆ ⊥
• Properties/roles
– Subproperty P1 ⊆ P2 hasDaughter ⊆ hasChild
– Equivalence P1 ≡ P2 cost ≡ price
– Inverse P1 ≡ P2- hasChild ≡ hasParent-
– Transitive P+ ⊆ P ancestor+ ⊆ ancestor
– Functional Τ ⊆ ≤1P T ⊆ ≤1hasMother
– InverseFunctional Τ ⊆ ≤1P- T ⊆ ≤1hasPassportID-
• Individuals
– Equivalence {x1} ≡ {x2} {oeg:OscarCorcho} ≡ {img:Oscar}
– Different {x1} ≡ ¬{x2} {john} ≡ ¬{peter}
• Most axioms are reducible to inclusion (∪)
– C ≡ D iff both C ⊆ D and D ⊆ C
– C disjoint D iff C ⊆ ¬D

34. LD4SC
Summer
School
7th
-­‐
12th
June,
Cercedilla,
Spain
Description Logics
Understand the meaning of universal and existential restrictions
- Decide which is the set that we are defining with different expressions, taking into account
Open and Close World Assumptions

35. LD4SC
Summer
School
7th
-­‐
12th
June,
Cercedilla,
Spain
Do we understand these constructors?
• ∃hasColleague.Lecturer
• ∀hasColleague.Lecturer
• ∃hasColleague.{Oscar}
Oscar
Lecturer
hasColleague
hasColleague
hasColleague
hasColleague
hasColleague
hasColleague
hasColleague
hasColleague

36. LD4SC
Summer
School
7th
-­‐
12th
June,
Cercedilla,
Spain
Formalisation. Some basic DL
modelling guidelines
• X must be Y, X is an Y that... à X ⊆ Y
• X is exactly Y, X is the Y that... à X ≡ Y
• X is not Y (not the same as X is whatever it is not Y) à X ⊆ ¬Y
• X and Y are disjoint à X ∩ Y ⊆ ⊥
• X is Y or Z à X ⊆Y∪Z
• X is Y for which property P has
only instances of Z as values à X ⊆ Y ∩ (∀P.Z)
• X is Y for which property P has at
least an instance of Z as a value à X ⊆ Y ∩ (∃P.Z)
• X is Y for which property P has at
most 2 values à X ⊆ Y∩ (≤ 2.P)
• Individual X is a Y à X∈Y

37. LD4SC
Summer
School
7th
-­‐
12th
June,
Cercedilla,
Spain
Exercise. Formalize in DL,
and then in OWL DL
1.
Concept
defini?ons:
– Neighbourhoods
and
city
districts
are
two
different
types
of
city
territorial
divisions
– Social
ac?vi?es
are
always
run
in
one
or
several
community
centers.
– A
sport
ac?vity
is
a
city
ac?vity
that
is
run
only
in
sport
centers.
– A
city
district
has
at
least
a
community
center,
and
every
community
center
belongs
to
a
district.
– Neighbourhoods
are
parts
of
a
city,
but
there
are
other
parts
of
a
city
that
are
not
neighbourhoods.
– An
empty
ac?vity
is
a
social
ac?vity
that
is
run
in
a
community
center
that
does
not
belong
to
any
district.
2.
Individuals:
– Waterloo
is
a
district.
– Nozng
Hill
is
a
neighbourhood
and
has
a
sport
center
“XX”.
– Elephant
and
Castle

38. LD4SC
Summer
School
7th
-­‐
12th
June,
Cercedilla,
Spain
Inference. Basic Inference Tasks
• Subsumption – check knowledge is correct (captures intuitions)
– Does C subsume D w.r.t. ontology O? (in every model I of O, CI ⊆ DI )
• Equivalence – check knowledge is minimally redundant (no unintended
synonyms)
– Is C equivalent to D w.r.t. O? (in every model I of O, CI = DI )
• Consistency – check knowledge is meaningful (classes can have instances)
– Is C satisfiable w.r.t. O? (there exists some model I of O s.t. CI ≠ ∅ )
• Instantiation and querying
– Is x an instance of C w.r.t. O? (in every model I of O, xI ∈ CI )
– Is (x,y) an instance of R w.r.t. O? (in every model I of O, (xI,yI) ∈ RI )
• All reducible to KB satisfiability or concept satisfiability w.r.t. a KB
• Can be decided using tableaux reasoners

39. LD4SC
Summer
School
7th
-­‐
12th
June,
Cercedilla,
Spain
What
are
we
going
to
do?
Specification
Modelling
GenerationPublication
Exploitation
Linking
39
For
the
week

40. LD4SC
Summer
School
7th
-­‐
12th
June,
Cercedilla,
Spain
Preparing
the
hands-­‐on
• Goal:
to
create
hand-­‐on
groups
• Sign
up
for
a
dataset
– Sheet
with
datasets
are
available
in
the
main
room
(with
sofas)
• Restric?ons
for
crea?ng
groups
– 3-­‐4
members
– At
least
1
computer
scien?st
• If
your
group
does
not
meet
the
restric?on
you
need
to
join
another
group
40

41. LD4SC
Summer
School
7th
-­‐
12th
June,
Cercedilla,
Spain
Final
presenta?on
• Friday:
group
projects
presenta?ons
• 5
slides
per
group
• Summarize
the
work
you
have
done
during
the
week
– Par?cipa?on
of
all
members
– Work
quality
– Presenta?on
– Fun
– …
• Prize
for
the
best
group!
41

42. LD4SC
Summer
School
7th
-­‐
12th
June,
Cercedilla,
Spain
LD4SC
Summer
School
7th
-­‐
12th
June,
Cercedilla,
Spain
Task
1
1. Extract
requirements
• Competency
ques?ons
• Data
analysis
2. Vocabulary
conceptualiza?on
3. Start
with
the
implementa?on
(Protégé)
For
today

43. LD4SC
Summer
School
7th
-­‐
12th
June,
Cercedilla,
Spain
Hands-­‐on
task
1
-­‐
Deliverables
• An
document
lis?ng
– The
competency
ques?ons
– List
of
terms
and
rela?onships
– Conceptualiza?on
(drawing)
• OWL
file
with
ontology
implementa?on
43
43

44. LD4SC
Summer
School
7th
-­‐
12th
June,
Cercedilla,
Spain
DATA
LEVEL
MODEL
LEVEL
Nota?on
Concept
A
Concept
B
Concept
A1
Concept
A2
<<subClassOf>>
aeribute::
datatype
rela?onship
Rela?on
between
two
individuals
à
object
property
or
just
“rela?onship”
Rela?on
between
one
individual
and
one
value
à
aeribute
Instance
1
<<type>>
Instance
2
rela?onship
Value^^datatype
aeribute
<<type>>