SlideShare uma empresa Scribd logo
1 de 16
Baixar para ler offline
Datalog+, RuleML and OWL 2:
Formats and Translations for Existential Rules
Jean-François Baget (baget@lirmm.fr)
Alain Gutierrez
Michel Leclère
Marie-Laure Mugnier
Swan Rocher
Clément Sipieter
PROJECT TEAM
GraphIK
INRIA Sophia-Antiplis
LIRMM
The 9th International Web Rule
Symposium (RuleML) 2015
An overview of GRAAL
• GRAAL: a platform for reasoning with Existential Rules/Datalog+
(see yesterday’s talk by C. Sipieter « Graal: A Toolkit for Query
Answering with Existential Rules»)
Facts
Ontology
Conjunctive
query
KnowledgeBase
GRAAL: Architecture
- 3
Yesterday’stalk
This talk
DLGP (DataLoG Plus) at a glance
• father(bob, tom).
• father(tom, X), parents(X, sam, alice).
• [A1] mother(bob, liz).
• father(X,Y), mother(X,Z) :- parents(X,Y,Z).
• parents(Y,U,V), parents (Z,R,S) :- parents(X,Y,Z).
• ! :- father(X,Y), mother(X,Y). % Constraint
• Y = Z :- mother(X,Y), mother(X,Z). % Equality Rule
- 4
FactsRules
A Natural Transformation in RuleML
- 5
[A2] parents(Y,U,V), parents (Z,R,S) :- parents(X,Y,Z).
<Assert><!-- A2 -->
<Forall><Var>X</Var><Var>Y</Var><Var>Z</Var>
<Implies>
<if>
<Atom><Rel>parents</Rel>
<Var>X</Var><Var>Y</Var><Var>Z</Var>
</Atom>
</if>
<then><Exists>
<Var>U</Var><Var>V</Var><Var>R</Var><Var>S</Var>
<And>
<Atom><Rel>parents</Rel>
<Var>Y</Var><Var>U</Var><Var>V</Var>
</Atom>
<Atom><Rel>parents</Rel>
<Var>Z</Var><Var>R</Var><Var>S</Var>
</Atom>
</And></Exists></then></Implies></Forall></Assert>
A (quick) overview of OWL 2
• Primitive Classes (unary predicates): Man, Woman, …
• Primitive Properties (binary predicates): father, mother, …
• Class expressions: ( parent. Human) ⊓ ( parent . God)
• Property Expressions: parent . mother
• Assertions:
– (( parent. Human) ⊓ ( parent . God))(hercules)
– grand-mother ≡ parent . mother
- 6
OWL2 and Existential Rules
- 7
OWL 2 Existential Rules
A ⊑ B ⊔ C
P(Y, T), r(Z, T) :- r(X, Y), p(X, Z)
q(a, b, c)
ER
RLQLEL
¬ A ⊑ B
Adding IRIs in DLGP
• constants and predicate names are now IRIs (turtle-
like syntax)
– absolute IRI: <http://example.org/pred>
– prefixed IRI:
• @prefix ex: <http://example.org>
• ex:pred
– relative IRI:
• @base <http://example.org>
• <pred>
• And we can still write (datalog compatibility): pred
- 8
From OWL 2 to DLGP
• Assertions without class expressions can always be translated
– parent . mother ⊑ grandmother
– grandmother(X,Z) :- parent (X,Y), mother(Y,Z).
• Translation of assertions with class expressions in inclusions
– (( parent. Human) ⊓ ( parent . God))(hercules)
– {hercules} ⊑ ( parent. Human) ⊓ ( parent . God)
- 9
Transformation into Class Inclusions
- 10
Analysis of Inclusions: principle
• A ⊑ B
– Can be translated as a single rule when A and B are EquivClass
expressions.
– When A is a SubClass expression and B is a SuperClass
expression, can be rewritten as a set of inclusions of form A’ ⊑ B’
where A’ and B’ are EquivClass expressions.
• The ER profile limits OWL 2 assertions to those whose
associated class inclusions are of form SubClass ⊑ SuperClass
- 11
EquivClass Expressions
- 12
SubClass Expressions
- 13
• Rationale: when A is a SubClass expression, A is the existential
closure of conjunctions and disjunctions. Its disjunctive normal
form is A1 ⊔ … ⊔ Ak where the Ai are EquivClass expressions.
So A ⊑ B is equivalent to the set of inclusions Ai ⊑ B.
SuperClass Expressions
• Example: A ⊑ ¬ B is equivalent to A ⊓ B ⊑ Nothing. When A
and B are SubClass expressions, A ⊓ B is also a SubClass
expression. Thus ¬ B is a SuperClass expression when B is
a SubClass expression.
- 14
Our algorithm: partial translation of
non ER assertions
- 15
A ⊔ ¬ B ⊑ ∀ r . (C ⊓ ¬ B) ⊓  r . (B ⊔ C)
A ⊔ ¬ B ⊑ ∀ r . (C ⊓ ¬ B) A ⊔ ¬ B ⊑  r . (B ⊔ C)
 r- . (A ⊔ ¬ B) ⊑ C ⊓ ¬ B
 r- . (A ⊔ ¬ B) ⊑ C  r- . (A ⊔ ¬ B) ⊑ ¬ B
 r- . (A ⊔ ¬ B) ⊓ B ⊑ ( r- . A) ⊔ ( r- . ¬ B) ⊑ C
( r- . A) ⊑ C  r- . ¬ B ⊑ C
C(X) :- r(Y,X), A(Y).
(( r- . A) ⊓ B) ⊔ (( r- . ¬ B) ⊓ B) ⊑ 
( r- . A) ⊓ B ⊑  ( r- . ¬ B) ⊓ B ⊑ 
! :- r(Y,X), A(Y), B(X).
https://graphik-team.github.io/graal
- 16

Mais conteúdo relacionado

Semelhante a Challenge@RuleML2015 Datalog+, RuleML and OWL 2 - Formats and Translations for Existential Rules

Cs344 lect15-robotic-knowledge-inferencing-prolog-11feb08
Cs344 lect15-robotic-knowledge-inferencing-prolog-11feb08Cs344 lect15-robotic-knowledge-inferencing-prolog-11feb08
Cs344 lect15-robotic-knowledge-inferencing-prolog-11feb08
Praveen Kumar
 
Introduction to Probabilistic Latent Semantic Analysis
Introduction to Probabilistic Latent Semantic AnalysisIntroduction to Probabilistic Latent Semantic Analysis
Introduction to Probabilistic Latent Semantic Analysis
NYC Predictive Analytics
 

Semelhante a Challenge@RuleML2015 Datalog+, RuleML and OWL 2 - Formats and Translations for Existential Rules (20)

#8 formal methods – pro logic
#8 formal methods – pro logic#8 formal methods – pro logic
#8 formal methods – pro logic
 
Pl vol1
Pl vol1Pl vol1
Pl vol1
 
ESWC 2013 Poster: Representing and Querying Negative Knowledge in RDF
ESWC 2013 Poster: Representing and Querying Negative Knowledge in RDFESWC 2013 Poster: Representing and Querying Negative Knowledge in RDF
ESWC 2013 Poster: Representing and Querying Negative Knowledge in RDF
 
Introducing: A Complete Algebra of Data
Introducing: A Complete Algebra of DataIntroducing: A Complete Algebra of Data
Introducing: A Complete Algebra of Data
 
Pl vol1
Pl vol1Pl vol1
Pl vol1
 
Introduction to query rewriting optimisation with dependencies
Introduction to query rewriting optimisation with dependenciesIntroduction to query rewriting optimisation with dependencies
Introduction to query rewriting optimisation with dependencies
 
Prolog,Prolog Programming IN AI.pdf
Prolog,Prolog Programming IN AI.pdfProlog,Prolog Programming IN AI.pdf
Prolog,Prolog Programming IN AI.pdf
 
Parallel Datalog Reasoning in RDFox Presentation
Parallel Datalog Reasoning in RDFox PresentationParallel Datalog Reasoning in RDFox Presentation
Parallel Datalog Reasoning in RDFox Presentation
 
Cs344 lect15-robotic-knowledge-inferencing-prolog-11feb08
Cs344 lect15-robotic-knowledge-inferencing-prolog-11feb08Cs344 lect15-robotic-knowledge-inferencing-prolog-11feb08
Cs344 lect15-robotic-knowledge-inferencing-prolog-11feb08
 
Programming picaresque
Programming picaresqueProgramming picaresque
Programming picaresque
 
PAGOdA paper
PAGOdA paperPAGOdA paper
PAGOdA paper
 
Tutorial - Introduction to Rule Technologies and Systems
Tutorial - Introduction to Rule Technologies and SystemsTutorial - Introduction to Rule Technologies and Systems
Tutorial - Introduction to Rule Technologies and Systems
 
Word2vec slide(lab seminar)
Word2vec slide(lab seminar)Word2vec slide(lab seminar)
Word2vec slide(lab seminar)
 
predicateLogic.ppt
predicateLogic.pptpredicateLogic.ppt
predicateLogic.ppt
 
An Introduction to RevBayes and Graphical Models
An Introduction to RevBayes and Graphical ModelsAn Introduction to RevBayes and Graphical Models
An Introduction to RevBayes and Graphical Models
 
2nd Proj. Update: Integrating SWI-Prolog for Semantic Reasoning in Bioclipse
2nd Proj. Update: Integrating SWI-Prolog for Semantic Reasoning in Bioclipse2nd Proj. Update: Integrating SWI-Prolog for Semantic Reasoning in Bioclipse
2nd Proj. Update: Integrating SWI-Prolog for Semantic Reasoning in Bioclipse
 
Introduction to Probabilistic Latent Semantic Analysis
Introduction to Probabilistic Latent Semantic AnalysisIntroduction to Probabilistic Latent Semantic Analysis
Introduction to Probabilistic Latent Semantic Analysis
 
PHP - Introduction to PHP
PHP -  Introduction to PHPPHP -  Introduction to PHP
PHP - Introduction to PHP
 
m7-logic.ppt
m7-logic.pptm7-logic.ppt
m7-logic.ppt
 
Prolog 01
Prolog 01Prolog 01
Prolog 01
 

Mais de RuleML

A software agent controlling 2 robot arms in co-operating concurrent tasks
A software agent controlling 2 robot arms in co-operating concurrent tasksA software agent controlling 2 robot arms in co-operating concurrent tasks
A software agent controlling 2 robot arms in co-operating concurrent tasks
RuleML
 

Mais de RuleML (20)

Aggregates in Recursion: Issues and Solutions
Aggregates in Recursion: Issues and SolutionsAggregates in Recursion: Issues and Solutions
Aggregates in Recursion: Issues and Solutions
 
A software agent controlling 2 robot arms in co-operating concurrent tasks
A software agent controlling 2 robot arms in co-operating concurrent tasksA software agent controlling 2 robot arms in co-operating concurrent tasks
A software agent controlling 2 robot arms in co-operating concurrent tasks
 
Port Clearance Rules in PSOA RuleML: From Controlled-English Regulation to Ob...
Port Clearance Rules in PSOA RuleML: From Controlled-English Regulation to Ob...Port Clearance Rules in PSOA RuleML: From Controlled-English Regulation to Ob...
Port Clearance Rules in PSOA RuleML: From Controlled-English Regulation to Ob...
 
RuleML 2015: When Processes Rule Events
RuleML 2015: When Processes Rule EventsRuleML 2015: When Processes Rule Events
RuleML 2015: When Processes Rule Events
 
RuleML 2015: Ontology Reasoning using Rules in an eHealth Context
RuleML 2015: Ontology Reasoning using Rules in an eHealth ContextRuleML 2015: Ontology Reasoning using Rules in an eHealth Context
RuleML 2015: Ontology Reasoning using Rules in an eHealth Context
 
RuleML 2015: Semantics of Notation3 Logic: A Solution for Implicit Quantifica...
RuleML 2015: Semantics of Notation3 Logic: A Solution for Implicit Quantifica...RuleML 2015: Semantics of Notation3 Logic: A Solution for Implicit Quantifica...
RuleML 2015: Semantics of Notation3 Logic: A Solution for Implicit Quantifica...
 
Challenge@RuleML2015 Developing Situation-Aware Applications for Disaster Man...
Challenge@RuleML2015 Developing Situation-Aware Applications for Disaster Man...Challenge@RuleML2015 Developing Situation-Aware Applications for Disaster Man...
Challenge@RuleML2015 Developing Situation-Aware Applications for Disaster Man...
 
Rule Generalization Strategies in Incremental Learning of Disjunctive Concepts
Rule Generalization Strategies in Incremental Learning of Disjunctive ConceptsRule Generalization Strategies in Incremental Learning of Disjunctive Concepts
Rule Generalization Strategies in Incremental Learning of Disjunctive Concepts
 
RuleML 2015 Constraint Handling Rules - What Else?
RuleML 2015 Constraint Handling Rules - What Else?RuleML 2015 Constraint Handling Rules - What Else?
RuleML 2015 Constraint Handling Rules - What Else?
 
RuleML2015 The Herbrand Manifesto - Thinking Inside the Box
RuleML2015 The Herbrand Manifesto - Thinking Inside the Box RuleML2015 The Herbrand Manifesto - Thinking Inside the Box
RuleML2015 The Herbrand Manifesto - Thinking Inside the Box
 
RuleML2015 PSOA RuleML: Integrated Object-Relational Data and Rules
RuleML2015 PSOA RuleML: Integrated Object-Relational Data and RulesRuleML2015 PSOA RuleML: Integrated Object-Relational Data and Rules
RuleML2015 PSOA RuleML: Integrated Object-Relational Data and Rules
 
Industry@RuleML2015: Norwegian State of Estate A Reporting Service for the St...
Industry@RuleML2015: Norwegian State of Estate A Reporting Service for the St...Industry@RuleML2015: Norwegian State of Estate A Reporting Service for the St...
Industry@RuleML2015: Norwegian State of Estate A Reporting Service for the St...
 
A Service for Improving the Assignments of Common Agriculture Policy Funds to...
A Service for Improving the Assignments of Common Agriculture Policy Funds to...A Service for Improving the Assignments of Common Agriculture Policy Funds to...
A Service for Improving the Assignments of Common Agriculture Policy Funds to...
 
Datalog+-Track Introduction & Reasoning on UML Class Diagrams via Datalog+-
Datalog+-Track Introduction & Reasoning on UML Class Diagrams via Datalog+-Datalog+-Track Introduction & Reasoning on UML Class Diagrams via Datalog+-
Datalog+-Track Introduction & Reasoning on UML Class Diagrams via Datalog+-
 
RuleML2015: Binary Frontier-guarded ASP with Function Symbols
RuleML2015: Binary Frontier-guarded ASP with Function SymbolsRuleML2015: Binary Frontier-guarded ASP with Function Symbols
RuleML2015: Binary Frontier-guarded ASP with Function Symbols
 
RuleML2015: API4KP Metamodel: A Meta-API for Heterogeneous Knowledge Platforms
RuleML2015: API4KP Metamodel: A Meta-API for Heterogeneous Knowledge PlatformsRuleML2015: API4KP Metamodel: A Meta-API for Heterogeneous Knowledge Platforms
RuleML2015: API4KP Metamodel: A Meta-API for Heterogeneous Knowledge Platforms
 
RuleML2015: Rule-Based Exploration of Structured Data in the Browser
RuleML2015: Rule-Based Exploration of Structured Data in the BrowserRuleML2015: Rule-Based Exploration of Structured Data in the Browser
RuleML2015: Rule-Based Exploration of Structured Data in the Browser
 
RuleML2015: Ontology-Based Multidimensional Contexts with Applications to Qua...
RuleML2015: Ontology-Based Multidimensional Contexts with Applications to Qua...RuleML2015: Ontology-Based Multidimensional Contexts with Applications to Qua...
RuleML2015: Ontology-Based Multidimensional Contexts with Applications to Qua...
 
RuleML2015: Compact representation of conditional probability for rule-based...
RuleML2015:  Compact representation of conditional probability for rule-based...RuleML2015:  Compact representation of conditional probability for rule-based...
RuleML2015: Compact representation of conditional probability for rule-based...
 
RuleML2015: Learning Characteristic Rules in Geographic Information Systems
RuleML2015: Learning Characteristic Rules in Geographic Information SystemsRuleML2015: Learning Characteristic Rules in Geographic Information Systems
RuleML2015: Learning Characteristic Rules in Geographic Information Systems
 

Último

Human genetics..........................pptx
Human genetics..........................pptxHuman genetics..........................pptx
Human genetics..........................pptx
Silpa
 
Phenolics: types, biosynthesis and functions.
Phenolics: types, biosynthesis and functions.Phenolics: types, biosynthesis and functions.
Phenolics: types, biosynthesis and functions.
Silpa
 
LUNULARIA -features, morphology, anatomy ,reproduction etc.
LUNULARIA -features, morphology, anatomy ,reproduction etc.LUNULARIA -features, morphology, anatomy ,reproduction etc.
LUNULARIA -features, morphology, anatomy ,reproduction etc.
Silpa
 
Asymmetry in the atmosphere of the ultra-hot Jupiter WASP-76 b
Asymmetry in the atmosphere of the ultra-hot Jupiter WASP-76 bAsymmetry in the atmosphere of the ultra-hot Jupiter WASP-76 b
Asymmetry in the atmosphere of the ultra-hot Jupiter WASP-76 b
Sérgio Sacani
 
(May 9, 2024) Enhanced Ultrafast Vector Flow Imaging (VFI) Using Multi-Angle ...
(May 9, 2024) Enhanced Ultrafast Vector Flow Imaging (VFI) Using Multi-Angle ...(May 9, 2024) Enhanced Ultrafast Vector Flow Imaging (VFI) Using Multi-Angle ...
(May 9, 2024) Enhanced Ultrafast Vector Flow Imaging (VFI) Using Multi-Angle ...
Scintica Instrumentation
 
Reboulia: features, anatomy, morphology etc.
Reboulia: features, anatomy, morphology etc.Reboulia: features, anatomy, morphology etc.
Reboulia: features, anatomy, morphology etc.
Silpa
 
development of diagnostic enzyme assay to detect leuser virus
development of diagnostic enzyme assay to detect leuser virusdevelopment of diagnostic enzyme assay to detect leuser virus
development of diagnostic enzyme assay to detect leuser virus
NazaninKarimi6
 
The Mariana Trench remarkable geological features on Earth.pptx
The Mariana Trench remarkable geological features on Earth.pptxThe Mariana Trench remarkable geological features on Earth.pptx
The Mariana Trench remarkable geological features on Earth.pptx
seri bangash
 

Último (20)

Climate Change Impacts on Terrestrial and Aquatic Ecosystems.pptx
Climate Change Impacts on Terrestrial and Aquatic Ecosystems.pptxClimate Change Impacts on Terrestrial and Aquatic Ecosystems.pptx
Climate Change Impacts on Terrestrial and Aquatic Ecosystems.pptx
 
Human genetics..........................pptx
Human genetics..........................pptxHuman genetics..........................pptx
Human genetics..........................pptx
 
Phenolics: types, biosynthesis and functions.
Phenolics: types, biosynthesis and functions.Phenolics: types, biosynthesis and functions.
Phenolics: types, biosynthesis and functions.
 
LUNULARIA -features, morphology, anatomy ,reproduction etc.
LUNULARIA -features, morphology, anatomy ,reproduction etc.LUNULARIA -features, morphology, anatomy ,reproduction etc.
LUNULARIA -features, morphology, anatomy ,reproduction etc.
 
Asymmetry in the atmosphere of the ultra-hot Jupiter WASP-76 b
Asymmetry in the atmosphere of the ultra-hot Jupiter WASP-76 bAsymmetry in the atmosphere of the ultra-hot Jupiter WASP-76 b
Asymmetry in the atmosphere of the ultra-hot Jupiter WASP-76 b
 
(May 9, 2024) Enhanced Ultrafast Vector Flow Imaging (VFI) Using Multi-Angle ...
(May 9, 2024) Enhanced Ultrafast Vector Flow Imaging (VFI) Using Multi-Angle ...(May 9, 2024) Enhanced Ultrafast Vector Flow Imaging (VFI) Using Multi-Angle ...
(May 9, 2024) Enhanced Ultrafast Vector Flow Imaging (VFI) Using Multi-Angle ...
 
Genome sequencing,shotgun sequencing.pptx
Genome sequencing,shotgun sequencing.pptxGenome sequencing,shotgun sequencing.pptx
Genome sequencing,shotgun sequencing.pptx
 
Human & Veterinary Respiratory Physilogy_DR.E.Muralinath_Associate Professor....
Human & Veterinary Respiratory Physilogy_DR.E.Muralinath_Associate Professor....Human & Veterinary Respiratory Physilogy_DR.E.Muralinath_Associate Professor....
Human & Veterinary Respiratory Physilogy_DR.E.Muralinath_Associate Professor....
 
Reboulia: features, anatomy, morphology etc.
Reboulia: features, anatomy, morphology etc.Reboulia: features, anatomy, morphology etc.
Reboulia: features, anatomy, morphology etc.
 
Cyanide resistant respiration pathway.pptx
Cyanide resistant respiration pathway.pptxCyanide resistant respiration pathway.pptx
Cyanide resistant respiration pathway.pptx
 
Dr. E. Muralinath_ Blood indices_clinical aspects
Dr. E. Muralinath_ Blood indices_clinical  aspectsDr. E. Muralinath_ Blood indices_clinical  aspects
Dr. E. Muralinath_ Blood indices_clinical aspects
 
CURRENT SCENARIO OF POULTRY PRODUCTION IN INDIA
CURRENT SCENARIO OF POULTRY PRODUCTION IN INDIACURRENT SCENARIO OF POULTRY PRODUCTION IN INDIA
CURRENT SCENARIO OF POULTRY PRODUCTION IN INDIA
 
Clean In Place(CIP).pptx .
Clean In Place(CIP).pptx                 .Clean In Place(CIP).pptx                 .
Clean In Place(CIP).pptx .
 
development of diagnostic enzyme assay to detect leuser virus
development of diagnostic enzyme assay to detect leuser virusdevelopment of diagnostic enzyme assay to detect leuser virus
development of diagnostic enzyme assay to detect leuser virus
 
PSYCHOSOCIAL NEEDS. in nursing II sem pptx
PSYCHOSOCIAL NEEDS. in nursing II sem pptxPSYCHOSOCIAL NEEDS. in nursing II sem pptx
PSYCHOSOCIAL NEEDS. in nursing II sem pptx
 
The Mariana Trench remarkable geological features on Earth.pptx
The Mariana Trench remarkable geological features on Earth.pptxThe Mariana Trench remarkable geological features on Earth.pptx
The Mariana Trench remarkable geological features on Earth.pptx
 
Zoology 5th semester notes( Sumit_yadav).pdf
Zoology 5th semester notes( Sumit_yadav).pdfZoology 5th semester notes( Sumit_yadav).pdf
Zoology 5th semester notes( Sumit_yadav).pdf
 
Genetics and epigenetics of ADHD and comorbid conditions
Genetics and epigenetics of ADHD and comorbid conditionsGenetics and epigenetics of ADHD and comorbid conditions
Genetics and epigenetics of ADHD and comorbid conditions
 
Selaginella: features, morphology ,anatomy and reproduction.
Selaginella: features, morphology ,anatomy and reproduction.Selaginella: features, morphology ,anatomy and reproduction.
Selaginella: features, morphology ,anatomy and reproduction.
 
FAIRSpectra - Enabling the FAIRification of Analytical Science
FAIRSpectra - Enabling the FAIRification of Analytical ScienceFAIRSpectra - Enabling the FAIRification of Analytical Science
FAIRSpectra - Enabling the FAIRification of Analytical Science
 

Challenge@RuleML2015 Datalog+, RuleML and OWL 2 - Formats and Translations for Existential Rules

  • 1. Datalog+, RuleML and OWL 2: Formats and Translations for Existential Rules Jean-François Baget (baget@lirmm.fr) Alain Gutierrez Michel Leclère Marie-Laure Mugnier Swan Rocher Clément Sipieter PROJECT TEAM GraphIK INRIA Sophia-Antiplis LIRMM The 9th International Web Rule Symposium (RuleML) 2015
  • 2. An overview of GRAAL • GRAAL: a platform for reasoning with Existential Rules/Datalog+ (see yesterday’s talk by C. Sipieter « Graal: A Toolkit for Query Answering with Existential Rules») Facts Ontology Conjunctive query KnowledgeBase
  • 4. DLGP (DataLoG Plus) at a glance • father(bob, tom). • father(tom, X), parents(X, sam, alice). • [A1] mother(bob, liz). • father(X,Y), mother(X,Z) :- parents(X,Y,Z). • parents(Y,U,V), parents (Z,R,S) :- parents(X,Y,Z). • ! :- father(X,Y), mother(X,Y). % Constraint • Y = Z :- mother(X,Y), mother(X,Z). % Equality Rule - 4 FactsRules
  • 5. A Natural Transformation in RuleML - 5 [A2] parents(Y,U,V), parents (Z,R,S) :- parents(X,Y,Z). <Assert><!-- A2 --> <Forall><Var>X</Var><Var>Y</Var><Var>Z</Var> <Implies> <if> <Atom><Rel>parents</Rel> <Var>X</Var><Var>Y</Var><Var>Z</Var> </Atom> </if> <then><Exists> <Var>U</Var><Var>V</Var><Var>R</Var><Var>S</Var> <And> <Atom><Rel>parents</Rel> <Var>Y</Var><Var>U</Var><Var>V</Var> </Atom> <Atom><Rel>parents</Rel> <Var>Z</Var><Var>R</Var><Var>S</Var> </Atom> </And></Exists></then></Implies></Forall></Assert>
  • 6. A (quick) overview of OWL 2 • Primitive Classes (unary predicates): Man, Woman, … • Primitive Properties (binary predicates): father, mother, … • Class expressions: ( parent. Human) ⊓ ( parent . God) • Property Expressions: parent . mother • Assertions: – (( parent. Human) ⊓ ( parent . God))(hercules) – grand-mother ≡ parent . mother - 6
  • 7. OWL2 and Existential Rules - 7 OWL 2 Existential Rules A ⊑ B ⊔ C P(Y, T), r(Z, T) :- r(X, Y), p(X, Z) q(a, b, c) ER RLQLEL ¬ A ⊑ B
  • 8. Adding IRIs in DLGP • constants and predicate names are now IRIs (turtle- like syntax) – absolute IRI: <http://example.org/pred> – prefixed IRI: • @prefix ex: <http://example.org> • ex:pred – relative IRI: • @base <http://example.org> • <pred> • And we can still write (datalog compatibility): pred - 8
  • 9. From OWL 2 to DLGP • Assertions without class expressions can always be translated – parent . mother ⊑ grandmother – grandmother(X,Z) :- parent (X,Y), mother(Y,Z). • Translation of assertions with class expressions in inclusions – (( parent. Human) ⊓ ( parent . God))(hercules) – {hercules} ⊑ ( parent. Human) ⊓ ( parent . God) - 9
  • 10. Transformation into Class Inclusions - 10
  • 11. Analysis of Inclusions: principle • A ⊑ B – Can be translated as a single rule when A and B are EquivClass expressions. – When A is a SubClass expression and B is a SuperClass expression, can be rewritten as a set of inclusions of form A’ ⊑ B’ where A’ and B’ are EquivClass expressions. • The ER profile limits OWL 2 assertions to those whose associated class inclusions are of form SubClass ⊑ SuperClass - 11
  • 13. SubClass Expressions - 13 • Rationale: when A is a SubClass expression, A is the existential closure of conjunctions and disjunctions. Its disjunctive normal form is A1 ⊔ … ⊔ Ak where the Ai are EquivClass expressions. So A ⊑ B is equivalent to the set of inclusions Ai ⊑ B.
  • 14. SuperClass Expressions • Example: A ⊑ ¬ B is equivalent to A ⊓ B ⊑ Nothing. When A and B are SubClass expressions, A ⊓ B is also a SubClass expression. Thus ¬ B is a SuperClass expression when B is a SubClass expression. - 14
  • 15. Our algorithm: partial translation of non ER assertions - 15 A ⊔ ¬ B ⊑ ∀ r . (C ⊓ ¬ B) ⊓  r . (B ⊔ C) A ⊔ ¬ B ⊑ ∀ r . (C ⊓ ¬ B) A ⊔ ¬ B ⊑  r . (B ⊔ C)  r- . (A ⊔ ¬ B) ⊑ C ⊓ ¬ B  r- . (A ⊔ ¬ B) ⊑ C  r- . (A ⊔ ¬ B) ⊑ ¬ B  r- . (A ⊔ ¬ B) ⊓ B ⊑ ( r- . A) ⊔ ( r- . ¬ B) ⊑ C ( r- . A) ⊑ C  r- . ¬ B ⊑ C C(X) :- r(Y,X), A(Y). (( r- . A) ⊓ B) ⊔ (( r- . ¬ B) ⊓ B) ⊑  ( r- . A) ⊓ B ⊑  ( r- . ¬ B) ⊓ B ⊑  ! :- r(Y,X), A(Y), B(X).