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Ontology Design Patterns for Linked Data Tutorial at ISWC2016 - Introduction
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 WebProtÊgÊ 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 GrĂźninger, MS Fox - BenchmarkingâTheory
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 classiďŹcations
⢠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 âexpert ďŹndingâ 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 SpeciďŹc 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
Whatâs an object made of? Drug and food composition, e.g. for safety (comp.)
Whatâs the place of something? Geographic systems, resource allocation
Whatâs 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, workďŹow management
Does this behaviour conform to a certain rule? Control systems, legal reasoning services
Whatâs the function of that artifact? System description
How is that object built? Control systems, quality check
Whatâs the design of that artifact? Project assistants, catalogues
How did that phenomenon happen? Diagnostic systems, physical models
Whatâs 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
Whatâs the degree of conďŹdence that you give to this axiom? Ontology engineering tools
Good news: competency questions and modelling
solutions can be generalised and morphed (Ă la
foundational ontology)
9. An ontology design pattern describes a formal expression
that can be exempliďŹed, morphed, instantiated, and expressed in
order to solve a domain modelling problem
⢠owl:Class:_:x rdfs:subClassOf owl:Restriction:_:y
⢠InďŹammation rdfs:subClassOf (localizedIn some BodyPart)
⢠Colitis rdfs:subClassOf (localizedIn some Colon)
⢠Johnâs_colitis isLocalizedIn Johnâs_colon
⢠âJohnâs colon is inďŹammatedâ,âJohn has got colitisâ,âColitis is the inďŹammation of
colonâ
Layered pattern morphisms
Logical
Pattern
(MBox)
Generic
Content
Pattern
(TBox)
SpeciďŹc
Content
Pattern
(TBox)
Data
Pattern
(ABox)
exempliďŹedAs 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)
11. ⢠Temporal indexing pattern
â (R(a,b))+t sentence indexing
⢠quads, external time stamps
â R(a,b)+t relation indexing
⢠reiďŹed n-ary relations (3D frames)
â R(a+t,b+t) individual indexing
⢠ďŹuents, 4D, tropes,âcontext slicesâ (4D frames)
â 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 ďŹuents 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
â˘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)
â Quinary relation, needs adaptation to OWL
⢠Methods: reiďŹcation, reuse of a generic knowledge pattern,
binary projections, identiďŹcation constraint
n-ary relation / Situation
Concrete scenario
Abstracted scenario
FOL formaliza7on
15. 15
⢠I want to represent that a car is composed of several parts
⢠part of â transitive property
⢠I also want to represent that each part can have âdirectâ
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 âdirectâ 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 ďŹow
⢠âŚ
More OWL Patterns
19. 19
Procedural patterns
⢠Precise
â ClassiďŹcation
â Subsumption
â Inheritance
â Materialization
â Rule ďŹring
â Constructive query
⢠Approximate
â Fuzzy classiďŹcation
â Information extraction (NER, RE)
â Similarity induction (e.g. alignment)
â Taxonomy induction
â Relevance detection
â 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 classiďŹcations 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-BlĂĄzquez, 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 âentailsâ IC)
⢠KaupthingBankBehavior isCauseOf IcelandCrisis
⢠Expressions as instances of the class representing their meaning
⢠*dog(word) rdf:type Dog
⢠dog(word) expresses Dog (with punning)
â˘Multiple domains or ranges of properties as intersection
⢠*hasInflammation rdfs:domain Epithelium ; Endothelium
⢠hasInflammation rdfs:domain (Epithelium or Endothelium)
â˘Collection membership as set membership
⢠*John_Coltrane rdf:type Miles_Davis_Group
⢠(since JC â Miles_Davis_Group)
⢠John_Coltrane memberOf Miles_Davis_Group
Anti-patterns (2/2)
23. Imagine we have to model the following
⢠Giovanni Sartor is the judge in the trial Berlusconi #57 that
is held at Ravennaâs 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
âBerlusconi #57â that is
held at Ravennaâs 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
âBerlusconi #57â that is
held at Ravennaâs 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
âBerlusconi #57â that is
held at Ravennaâs 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
âBerlusconi #57â that is
held at Ravennaâs 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
âBerlusconi #57â that is
held at Ravennaâs 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
âBerlusconi #57â that is
held at Ravennaâs 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
âBerlusconi #57â that is
held at Ravennaâs 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
âBerlusconi #57â that is
held at Ravennaâs 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
âBerlusconi #57â that is
held at Ravennaâs 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
34. 34
⢠DeďŹnition: 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,âŚ)
⢠DL? OWL?
The Role Relation
35. 35
⢠Role as class
⢠Role as individual
⢠Role as property
⢠Role as time-indexed situation (n-ary reiďŹcation pattern)
⢠Role as trope (or qua-entity)
Role patterns
36. 36
⢠role(x,y,z,âŚ)
⢠â
⢠role(x)
⢠John_Coltrane â SaxPlayer
⢠(additional axioms)
⢠John_Coltrane â Person
⢠SaxPlayer â Person
⢠SaxPlayer ⣠Person ⨠âplays.Sax
â˘
(alt) SaxPlayer ⣠Person ⨠âplays.{Sax}
â˘
Sax â Instrument || Sax â Instrument
⢠âŚ
Role as class
Notice the type reiďŹcation,
more frequently used with
products, substances, etc.
39. 39
Role as property
⢠role(x,y,z,âŚ)
⢠â
⢠role(x,z)
⢠(John_Coltrane, Sax) â player
⢠(additional axioms)
⢠John_Coltrane â Person
⢠Sax â Instrument
⢠player â Person X Instrument
Notice the type reiďŹcation
40. 40
Role as time-indexed situation
⢠role(x,y,z,âŚ)
⢠â
⢠situation(s) â r1
(s,x) â r2
(s,y) â r3
(s,z) â r4
(s,t) â âsâ((r1
(sâ,x) â r2
(sâ,y) â r3
(sâ,z) â r4
(sâ,t)) s = sâ )
⢠JohnColtraneAtKindOfBlueSessions â Situation
⢠(JohnColtraneAtKindOfBlueSessions, John_Coltrane) â r1
⢠(JohnColtraneAtKindOfBlueSessions, Player) â r2
⢠(JohnColtraneAtKindOfBlueSessions, Sax) â r3
⢠(JohnColtraneAtKindOfBlueSessions, March.2.1959) â r4
⢠(JohnColtraneAtKindOfBlueSessions,April.22.1959) â r4
⢠Situation ⣠âĽ1r1
⨠âĽ1r2
⨠âĽ1r3
⨠âĽ1r4
⢠(additional axioms)
⢠John_Coltrane â Person
⢠Player â Role
⢠Sax â Instrument
⢠March.2.1959 â Date
Notice the relation
reiďŹcation
Notice the
projections
Notice the key
(id constraint)
41. 41
Role as trope /qua-entity
⢠role(x,y,z,âŚ)
⢠â
⢠hasTrope(x,q) â hasRole(q,r) â hasInstrument(r,i)
⢠JohnColtraneAsSaxPlayer â Trope
⢠(John_Coltrane, JohnColtraneAsSaxPlayer) â hasTrope
⢠(JohnColtraneAsSaxPlayer, Player) â hasRole
⢠(additional axioms)
⢠hasTrope o hasRole o hasInstrument â plays
⢠John_Coltrane â Person
⢠Sax â hasInstrument
⢠SaxPlayer â Role
⢠Sax â Instrument
Notice the
property chain:
rel1 o rel2 o rel3
A trope is a âsliceâ
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)
43. 43
â˘Content patterns improve the quality of ontologies
â Experiments with master and PhD students
â 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
â˘eXtreme Design method further improves quality and also
improves coverage of the proposed requirements
â 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
â˘ODP-based ontology learning improves results
â˘Ontologies are better in terms of cohesion, consistency,
functional quality, etc.
â˘Experiment with OntoCase applied to Text2Onto ontology
learning
Eva Blomqvist, ISWC2009
Experimental evidence (III)
46. Paulheim, H. and Gangemi, A. Serving DBpedia with DOLCE â More than Just Adding a Cherry on Top.
Proceedings of ISWC2015, the Thirteenth International Semantic Web Conference, LNCS, Springer, 2015
Experimental evidence (IV)
47.
48.
49.
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.