This is the slides for the introduction to the tutotrial on Ontology Design Patterns for Linked Data Publishing at ISWC2016, Kobe, Japan

1. 1
• Introduction to ODPs - Aldo Gangemi (~30 mins including 10
mins of questions)
• How to document and evaluate/choose ODPs - Karl
Hammar (~30 min including 10 mins of questions)
• Methods to reuse ODPs -Valentina Presutti (~30 min
including 10 mins of questions)
• Break
• Examples in the Chess domain - Pascal Hitzler (~45 min)
• Examples in CH and/or eGov - Giorgia Lodi (~45 min)
• Intro to WebProtégé XDP plugin - Karl Hammar (~15 min)
• Hands-on - All tutors available for support (~2h30mins)
Agenda

2. Introduction to ODPs
Aldo Gangemi
1LIPN, Paris Nord University, CNRS UMR7030, France
2Semantic Technology Lab, ISTC-CNR, Rome, Italy
ODP Tutorial, Kobe

3. 3
• Assumption: some experience in ontology and (linked) data
design, predicate logic, RDF, OWL
• Summary
• Basics of ontology design and patterns
• Examples of knowledge patterns
• Examples of procedural patterns
• Anti-patterns
• eXtreme Design
• An exercise with role modelling
• Experimental support
Outline

4. quality, patterns:
methods rather than methodologies
The role of competency questions
in enterprise engineering
M Grüninger, MS Fox - Benchmarking—Theory
and Practice, 1995 - Springer
Modelling ontology
evaluation and validation
A Gangemi, C Catenacci, M Ciaramita, J
Lehmann - 2006 - Springer
Evaluating ontological
decisions with OntoClean
N Guarino, C Welty - Communications of
the ACM, 2002 - dl.acm.org
Ontology design patterns
A Gangemi, V Presutti - Handbook on
ontologies 2nd ed., 2009 - Springer

5. Ontology Design Patterns
An ontology design
pattern is a reusable
successful solution to
a recurrent modeling
problem
Visit www.ontologydesignpa/erns.org

6. 6
• Different classifications
• Basically, two main types
• Alexandrian (or procedural) patterns
• Minskyan (or knowledge) patterns
• Alexandrian patterns concern primarily the methods
• Minskyan patterns concern primarily the reusable models
Pattern types

7. 7
• Modeling problems with CPs have two main components: a
domain and some requirements.
• A same domain can have many requirements (e.g. different scenarios in a clinical
information context)
• A same requirement can be found in different domains (e.g. different domains
with a same “expert finding” scenario)
• A typical way of capturing requirements (and part of domain
terminology) is by means of competency questions
Components of ODP modelling

8. 8
Maximal ontology design requirements:
What are we talking about, and why?
Generic Competency Questions Specific Modelling Use Case
Who does what, when and where? Production reports, schedules
Which objects take part in a certain event? Resource allocation, biochemical pathways
What are the parts of something? Component schemas, warehouse management
What’s an object made of? Drug and food composition, e.g. for safety (comp.)
What’s the place of something? Geographic systems, resource allocation
What’s the time frame of something? Dynamic knowledge bases
What technique, method, practice is being used? Instructions, enterprise know-how database
Which tasks should be executed in order to achieve a certain goal? Planning, workflow management
Does this behaviour conform to a certain rule? Control systems, legal reasoning services
What’s the function of that artifact? System description
How is that object built? Control systems, quality check
What’s the design of that artifact? Project assistants, catalogues
How did that phenomenon happen? Diagnostic systems, physical models
What’s your role in that transaction? Activity diagrams, planning, organizational models
What that information is about? How is it realized? Information and content modelling, computational models, subject
directoriesWhat argumentation model are you adopting for negotiating an agreement? Cooperation systems
What’s the degree of confidence that you give to this axiom? Ontology engineering tools
Good news: competency questions and modelling
solutions can be generalised and morphed (à la
foundational ontology)

9. An ontology design pattern describes a formal expression
that can be exemplified, morphed, instantiated, and expressed in
order to solve a domain modelling problem
• owl:Class:_:x rdfs:subClassOf owl:Restriction:_:y
• Inflammation rdfs:subClassOf (localizedIn some BodyPart)
• Colitis rdfs:subClassOf (localizedIn some Colon)
• John’s_colitis isLocalizedIn John’s_colon
• “John’s colon is inflammated”,“John has got colitis”,“Colitis is the inflammation of
colon”
Layered pattern morphisms
Logical
Pattern
(MBox)
Generic
Content
Pattern
(TBox)
Specific
Content
Pattern
(TBox)
Data
Pattern
(ABox)
exemplifiedAs morphedAs instantiatedAs Linguistic
Pattern
expressedAs
Logic Meaning Reference Expression
expressedAs
Abstraction
Peter Clark, Bruce Porter: Knowledge Patterns, KR (2000)
Aldo Gangemi,Valentina Presutti: Ontology Design Patterns. Handbook on Ontologies 2nd ed. (2009)

10. 10
Examples
#1 Knowledge patterns

11. • Temporal indexing pattern
– (R(a,b))+t sentence indexing
• quads, external time stamps
– R(a,b)+t relation indexing
• reified n-ary relations (3D frames)
– R(a+t,b+t) individual indexing
• fluents, 4D, tropes,“context slices” (4D frames)
– tR name nesting
• ad hoc naming of binary relations
• More indexes for additional arguments
Alternative temporal n-ary patterns
A Multi-dimensional Comparison of Ontology Design Patterns for Representing n-ary Relations. A Gangemi,V Presutti. SOFSEM 2013: 86-105
An Empirical Perspective on Representing Time. A Scheuermann, E Motta, P Mulholland, A Gangemi andV Presutti. K-CAP 2013
Formal Unifying Standards for the Representation of Spatiotemporal Knowledge. P. Hayes, Advanced Decision Architectures Alliance, 2004
A reusable ontology for fluents in OWL. C Welty, R Fikes, S Makarios. FOIS, Springer, 2006

12. 12
• Radiolaria are found as zooplankton since the Cambrian
• Quad
• dbr:Radiolaria :foundAs dbr:Zooplankton dbr:Cambrian .
• Situation
• :find_1 :theme dbr:Radiolaria ; :habitat dbr:Zooplankton ; :since
dbr:Cambrian .
• Fluent
• dbr:Radiolaria_1 :foundAs dbr:Zooplankton_1 ; :since dbr:Cambrian
.
• Ad hoc naming
• dbr:Radiolaria :foundAsInCambrian dbr:Zooplankton .
In LD practice

13. 13
• Chad Smith was the drum player of Red Hot Chili
Peppers when they recorded their album Stadium
Arcadium from September 2004 to December 2005
•A person plays a certain role in a band during an
album recording, taking place during a certain time
interval
• PlaySituation(person, musicianrole, band,
album, timeinterval)
– Quinary relation, needs adaptation to OWL
• Methods: reification, reuse of a generic knowledge pattern,
binary projections, identification constraint
n-ary relation / Situation
Concrete scenario
Abstracted scenario
FOL formaliza7on

14. 14
• PlaySituation ∈ owl:Class
• PlaySituation ⊑ sit:Situation
• personPlaying ⊑ (PlaySituation ⨉ Person)
• playsRole ⊑ (PlaySituation ⨉ MusicianRole)
• inBand ⊑ (PlaySituation ⨉ Band)
• forAlbum ⊑ (PlaySituation ⨉ Album)
• recordingTime ⊑ (PlaySituation ⨉ tim:TimeInterval)
• PlaySituation hasKey[playsRole, forAlbum, inBand,
recordingTime, personPlaying]
(Intensional) reifica7on
Binary projec7on
Iden7fica7on constraint
Knowledge paCern specializa7on
n-ary relation / Situation

15. 15
• I want to represent that a car is composed of several parts
• part of – transitive property
• I also want to represent that each part can have “direct”
components
• e.g. the turbine is a component of the engine
• The turbine is a component of the engine, hence it is part of
the car, but not as “direct” component
Transitive Reduction

16. 16
direct componency still inherits partonymy, but not transitivity
Direct components in a car
partOf
partOf partOf

17. 17
• Transitive part-of
• Object vs. Event (3D+1)
• Situation
• Judgment communication
• Structure, Function, Process (GO)
• Linnaean taxonomy
• Invoicing
• Resource abundance observation
• Trajectory
• Control flow
• …
More OWL Patterns

18. 18
Examples
#2 Procedural patterns

19. 19
Procedural patterns
• Precise
– Classification
– Subsumption
– Inheritance
– Materialization
– Rule firing
– Constructive query
• Approximate
– Fuzzy classification
– Information extraction (NER, RE)
– Similarity induction (e.g. alignment)
– Taxonomy induction
– Relevance detection
– Latent semantic indexing
• Thesaurus to SKOS
• Relational DB to RDF
• WordNet RDB to OWL
• XML to RDF
• FrameNet XML to RDF
• Microformat to RDF
• NER entities to ABox
• NLP to RDF
Reasoning patterns
Alignment patterns
Reengineering patterns

20. 20
• Partonomies or subject classifications as subsumption hierarchies
• *City subClassOf Country
• City subClassOf (partOf some Country)
• *City subClassOf Geography
• City broader Geography (e.g. in SKOS)
• Linguistic disjunction as class disjointness
• Dead or alive
• *Dead or Alive
• Dead disjointWith Alive
• Linguistic conjunction as class disjunction
• Pen and paper
• *Pen and Paper
• Pen or Paper | Collection subClassOf (hasMember some Paper ; some Pen)
A catalogue of OWL ontology antipatterns.
Roussey, Corcho, Vilches-Blázquez, ACM, 2009.
A user oriented owl development environment designed
to implement common patterns and minimise common
errors. Horridge, Rector, Drummond, Springer, 2004.
Anti-patterns (1/2)

21. 21
• Causality as entailment
• Kaupthing bank behavior caused Iceland crisis
• *KaupthingBankBehavior subClassOf IcelandCrisis
• (since KBB “entails” IC)
• KaupthingBankBehavior isCauseOf IcelandCrisis
• Expressions as instances of the class representing their meaning
• *dog(word) rdf:type Dog
• dog(word) expresses Dog (with punning)
•Multiple domains or ranges of properties as intersection
• *hasInflammation rdfs:domain Epithelium ; Endothelium
• hasInflammation rdfs:domain (Epithelium or Endothelium)
•Collection membership as set membership
• *John_Coltrane rdf:type Miles_Davis_Group
• (since JC ∈ Miles_Davis_Group)
• John_Coltrane memberOf Miles_Davis_Group
Anti-patterns (2/2)

22. eXtreme Design (XD)

23. Imagine we have to model the following
• Giovanni Sartor is the judge in the trial Berlusconi #57 that
is held at Ravenna’s court during September and October
2030
23

24. Analyze the sentence, detect the
modeling issues, and match to the CPs
• Giovanni Sartor is the
judge in the trial
“Berlusconi #57” that is
held at Ravenna’s court
during September and
October 2030
• A person plays a role
24

25. Analyze the sentence, detect the
modeling issues, and match to the CPs
• Giovanni Sartor is the
judge in the trial
“Berlusconi #57” that is
held at Ravenna’s court
during September and
October 2030
• A person plays a role
25

26. Analyze the sentence, detect the
modeling issues, and match to the CPs
• Giovanni Sartor is the
judge in the trial
“Berlusconi #57” that is
held at Ravenna’s court
during September and
October 2030
• A person plays a role
26
• To represent objects and
the roles they play.

27. Analyze the sentence, detect the modeling
issues, and match to the CPs
• Giovanni Sartor is the
judge in the trial
“Berlusconi #57” that is
held at Ravenna’s court
during September and
October 2030
• The execution of some
procedure
27

28. Analyze the sentence, detect the modeling
issues, and match to the CPs
• Giovanni Sartor is the
judge in the trial
“Berlusconi #57” that is
held at Ravenna’s court
during September and
October 2030
• The execution of some
procedure
• To distinguish procedures from
their concrete executions.
28

29. Analyze the sentence, detect the modeling
issues, and match to the CPs
• Giovanni Sartor is the
judge in the trial
“Berlusconi #57” that is
held at Ravenna’s court
during September and
October 2030
• A time period
29

30. Analyze the sentence, detect the modeling
issues, and match to the CPs
• Giovanni Sartor is the
judge in the trial
“Berlusconi #57” that is
held at Ravenna’s court
during September and
October 2030
• A time period
• To represent time intervals, their
start/end dates, and any dates
falling into the period
30

31. Analyze the sentence, detect the modeling
issues, and match to the CPs
• Giovanni Sartor is the
judge in the trial
“Berlusconi #57” that is
held at Ravenna’s court
during September and
October 2030
• A person plays a role in a
trial, held at a court during a
time period
• How can we relate them
together?
31

32. Analyze the sentence, detect the modeling
issues, and match to the CPs
• Giovanni Sartor is the
judge in the trial
“Berlusconi #57” that is
held at Ravenna’s court
during September and
October 2030
• A person plays a role in a
trial, held at a court during a
time period
• To represent a situation, a set of
circumstances in a defined
setting
32

33. 33
Ontology design for
roles: an exercise in
flexibility

34. 34
• Definition: something (of a certain type) can play a role at a
certain time, place, in a certain way, with something else, etc.
• Example: John Coltrane was the sax player in the Miles
Davis Quintet during the recording of the Kind of Blue
album (for the tracks SoWhat, Freddie Freeloader,All
Blues) for Columbia Records. Recording sessions took
place at Columbia's 30th Street Studio in NewYork
City on March 2 and April 22, 1959
• FOL: role(x,y,z,…)
• DL? OWL?
The Role Relation

35. 35
• Role as class
• Role as individual
• Role as property
• Role as time-indexed situation (n-ary reification pattern)
• Role as trope (or qua-entity)
Role patterns

36. 36
• role(x,y,z,…)
• →
• role(x)
• John_Coltrane ∈ SaxPlayer
• (additional axioms)
• John_Coltrane ∈ Person
• SaxPlayer ⊑ Person
• SaxPlayer ≣ Person ⨅ ∃plays.Sax
•
(alt) SaxPlayer ≣ Person ⨅ ∃plays.{Sax}
•
Sax ⊑ Instrument || Sax ∈ Instrument
• …
Role as class
Notice the type reification,
more frequently used with
products, substances, etc.

37. 37
Role as individual
• role(x,y,z,…)
• →
• hasRole(x,role)
• (John_Coltrane, SaxPlayer) ∈ hasRole
• (additional axioms)
• John_Coltrane ∈ Person
• SaxPlayer ∈ Role
• SaxPlayer ∈ hasRole⁻(isRoleOf) ⨅ ∃plays.Sax
• (alt) SaxPlayer ≣ Person ⨅ ∃plays.{Sax}
• Sax ⊑ Instrument || Sax ∈ Instrument
• ∃hasRole.SaxPlayer ⊑ ∃plays.Sax

38. 38
Pattern combination
• SaxPlayer ≣ ∃hasRole.SaxPlayer

39. 39
Role as property
• role(x,y,z,…)
• →
• role(x,z)
• (John_Coltrane, Sax) ∈ player
• (additional axioms)
• John_Coltrane ∈ Person
• Sax ∈ Instrument
• player ⊑ Person X Instrument
Notice the type reification

40. 40
Role as time-indexed situation
• role(x,y,z,…)
• →
• situation(s) ⋀ r1
(s,x) ⋀ r2
(s,y) ⋀ r3
(s,z) ⋀ r4
(s,t) ⋀ ∀s’((r1
(s’,x) ⋀ r2
(s’,y) ⋀ r3
(s’,z) ⋀ r4
(s’,t)) s = s’ )
• JohnColtraneAtKindOfBlueSessions ∈ Situation
• (JohnColtraneAtKindOfBlueSessions, John_Coltrane) ∈ r1
• (JohnColtraneAtKindOfBlueSessions, Player) ∈ r2
• (JohnColtraneAtKindOfBlueSessions, Sax) ∈ r3
• (JohnColtraneAtKindOfBlueSessions, March.2.1959) ∈ r4
• (JohnColtraneAtKindOfBlueSessions,April.22.1959) ∈ r4
• Situation ≣ ≥1r1
⨅ ≥1r2
⨅ ≥1r3
⨅ ≥1r4
• (additional axioms)
• John_Coltrane ∈ Person
• Player ∈ Role
• Sax ∈ Instrument
• March.2.1959 ∈ Date
Notice the relation
reification
Notice the
projections
Notice the key
(id constraint)

41. 41
Role as trope /qua-entity
• role(x,y,z,…)
• →
• hasTrope(x,q) ⋀ hasRole(q,r) ⋀ hasInstrument(r,i)
• JohnColtraneAsSaxPlayer ∈ Trope
• (John_Coltrane, JohnColtraneAsSaxPlayer) ∈ hasTrope
• (JohnColtraneAsSaxPlayer, Player) ∈ hasRole
• (additional axioms)
• hasTrope o hasRole o hasInstrument ⊑ plays
• John_Coltrane ∈ Person
• Sax ∈ hasInstrument
• SaxPlayer ∈ Role
• Sax ∈ Instrument
Notice the
property chain:
rel1 o rel2 o rel3
A trope is a “slice”
of an object
In principle, a trope can be used
instead of a situation by adding
axioms and keys to q (but has a
different intensional intuition, see
slide about time-indexing patterns)

42. 42
Experimental evidence

43. 43
•Content patterns improve the quality of ontologies
– Experiments with master and PhD students
– Quality measured in terms of
• task-coverage
• error-freedom
• subjective perception of smooth and good design
Blomqvist E., Gangemi A., Presutti V. Experiments in Pattern-based Ontology Design,
Proceedings of KCAP09, Los Angeles, ACM Press, 2009
Experimental evidence (I)

44. 44
•eXtreme Design method further improves quality and also
improves coverage of the proposed requirements
– Experiment with 7 designer pairs (PhD students)
Blomqvist E., Gangemi A., Daga E., Presutti V.. Experimenting with eXtreme Design. P.
Cimiano and S: Pinto (eds.): Proceedings of the Conference on Knowledge Engineering
and Knowledge Management (EKAW2010), LNCS, Springer, 2010
Experimental evidence (II)

45. 45
•ODP-based ontology learning improves results
•Ontologies are better in terms of cohesion, consistency,
functional quality, etc.
•Experiment with OntoCase applied to Text2Onto ontology
learning
Eva Blomqvist, ISWC2009
Experimental evidence (III)

46. Paulheim, H. and Gangemi, A. Serving DBpedia with DOLCE – More than Just Adding a Cherry on Top.
Proceedings of ISWC2015, the Thirteenth International Semantic Web Conference, LNCS, Springer, 2015
Experimental evidence (IV)

50. Pattern induction from data:
centrality discovery in datasets
mo:Track
mo:MusicAr.st
mo:Playlist
mo:Torrent
mo:ED2K
tags:Tag
mo:Record
foaf:maker
rdfs:Literal
dc:6tle
dc:datemo:image
dc:descrip6on
mo:track
tags:taggedWithTag
mo:available_as
mo:available_as
mo:available_as
Extrac.ng Core Knowledge from Linked
Data.
PresuQ, Aroyo et al., COLD2011.