SlideShare una empresa de Scribd logo
1 de 60
Descargar para leer sin conexión
Explanations for
DL-Lite
Alexander Borgida 2
Diego Calvanese 1
Mariano Rodríguez-Muro 1
1
1 Free University of Bozen Bolzano
2 Rutgers University
ODBASE 08 - Monterrey, México
Thursday, August 8, 13
Outline
• Explanations
• DL-Lite and Explanations for DL-Lite
• Explanations for traditional services
• Explanations for conjunctive queries
• Conclusions and future work
2 ODBASE 08 - Monterrey, México
Thursday, August 8, 13
Explanations
3 ODBASE 08 - Monterrey, México
Thursday, August 8, 13
Explanations
Why explanations?
3 ODBASE 08 - Monterrey, México
Thursday, August 8, 13
Explanations
Why explanations?
What are explanations?
3 ODBASE 08 - Monterrey, México
Thursday, August 8, 13
Explanations
Why explanations?
What are explanations?
• Explanations are formal proofs, constructed
from premises using rules of inference.
3 ODBASE 08 - Monterrey, México
Thursday, August 8, 13
Explanations
Why explanations?
What are explanations?
• Explanations are formal proofs, constructed
from premises using rules of inference.
3
Features of an explanation:
ODBASE 08 - Monterrey, México
Thursday, August 8, 13
Explanations
Why explanations?
What are explanations?
• Explanations are formal proofs, constructed
from premises using rules of inference.
3
Features of an explanation:
• Style
• Length
• Presentation
ODBASE 08 - Monterrey, México
Thursday, August 8, 13
Explanations (cont.)
4 ODBASE 08 - Monterrey, México
Thursday, August 8, 13
Explanations (cont.)
Audience: KB developer, End user
4 ODBASE 08 - Monterrey, México
Thursday, August 8, 13
Explanations (cont.)
Audience: KB developer, End user
4
1. Style. Understandable inference rules (NOT
refutation or resolution)
ODBASE 08 - Monterrey, México
Thursday, August 8, 13
Explanations (cont.)
Audience: KB developer, End user
4
1. Style. Understandable inference rules (NOT
refutation or resolution)
2. Length: 'shorter' preferred
ODBASE 08 - Monterrey, México
Thursday, August 8, 13
Explanations (cont.)
Audience: KB developer, End user
4
1. Style. Understandable inference rules (NOT
refutation or resolution)
2. Length: 'shorter' preferred
3. Presentation: Complete proof vs Iterative process. as
indicated by the user. Possibly eliminating 'obvious'
parts (not addressed here)
ODBASE 08 - Monterrey, México
Thursday, August 8, 13
DL-Litef
Concept constructs
B ::= A | ƎP | ƎP-
C::= B | ¬B | C1 ⊓ C2
TBox Assertions
B ⊑ C
(funct P) (funt P-)
ABox Assertions
A(a) R(a,b)
5 ODBASE 08 - Monterrey, México
Thursday, August 8, 13
DL-Litef
Is-A Hierarchies
Class disjointness
Role-typing
Participation constraints
Non-participation constraints
Functionality restrictions
A1 ⊑ A2
A1 ⊑ ¬A2
ƎP ⊑ A1
ƎP- ⊑ A1
A1 ⊑ ƎP
A1 ⊑ ƎP-
A1 ⊑ ¬ƎP
A1 ⊑ ¬ƎP-
(funct P) (funct P-)
6 ODBASE 08 - Monterrey, México
Thursday, August 8, 13
DL-Litef
A1 ⊑ A2
A1 ⊑ ¬A2
ƎP ⊑ A1
ƎP- ⊑ A1
A1 ⊑ ƎP
A1 ⊑ ƎP-
A1 ⊑ ¬ƎP
A1 ⊑ ¬ƎP-
(funct P) (funct P-)
A1 ⊑ A2
disjoint(A1,A2)
domain(P) ⊑ A1
range(P) ⊑ A1
A1 ⊑ domain(P)
A1 ⊑ range(P)
disjoint(A1, domain(P))
disjoint(A1, range(P))
(funct P) (funct P-)
6 ODBASE 08 - Monterrey, México
Thursday, August 8, 13
Reasoning services
7 ODBASE 08 - Monterrey, México
Thursday, August 8, 13
Reasoning services
• Standard Inferences
• TBox reasoning (concept consistency, subsumption)
• ABox reasoning (KB satisfiability, Instance checking*)
7 ODBASE 08 - Monterrey, México
Thursday, August 8, 13
Reasoning services
• Standard Inferences
• TBox reasoning (concept consistency, subsumption)
• ABox reasoning (KB satisfiability, Instance checking*)
✦ Finite model reasoning
7 ODBASE 08 - Monterrey, México
Thursday, August 8, 13
Reasoning services
• Standard Inferences
• TBox reasoning (concept consistency, subsumption)
• ABox reasoning (KB satisfiability, Instance checking*)
✦ Finite model reasoning
• Conjunctive Query Answering
7 ODBASE 08 - Monterrey, México
Thursday, August 8, 13
Reasoning services
• Standard Inferences
• TBox reasoning (concept consistency, subsumption)
• ABox reasoning (KB satisfiability, Instance checking*)
✦ Finite model reasoning
• Conjunctive Query Answering
• Successful queries
• Failed queries
7 ODBASE 08 - Monterrey, México
Thursday, August 8, 13
B C
A
D
Concept Subsumption
8 ODBASE 08 - Monterrey, México
Thursday, August 8, 13
B C
A
D
Concept Subsumption
• Hierarchy Traversing
8 ODBASE 08 - Monterrey, México
Thursday, August 8, 13
B C
A
D
Concept Subsumption
• Hierarchy Traversing
A ⊑ D ⊓ C
8 ODBASE 08 - Monterrey, México
Thursday, August 8, 13
B C
A
D
Concept Subsumption
• Hierarchy Traversing
A ⊑ D ⊓ C
A ⊑ C
A ⊑ B
B ⊑ D
8 ODBASE 08 - Monterrey, México
Thursday, August 8, 13
B C
A
D
Concept Subsumption
• Hierarchy Traversing
A ⊑ D ⊓ C
A ⊑ C
A ⊑ B
B ⊑ D
• Minimal size explanations
8 ODBASE 08 - Monterrey, México
Thursday, August 8, 13
B1
C2
C1
A
C3
B2
B3
disjoint
n j
9
A is unsatisfiable
ODBASE 08 - Monterrey, México
Thursday, August 8, 13
B1
C2
C1
A
C3
B2
B3
disjoint
n j
9
Explanation
length
n + j = 6
A is unsatisfiable
ODBASE 08 - Monterrey, México
Thursday, August 8, 13
B1
C2
C1
A
E3
E2
E1D1
C3
E4
B2
B3
disjoint
disjoint
n j k l
10
A is unsatisfiable
ODBASE 08 - Monterrey, México
Thursday, August 8, 13
B1
C2
C1
A
E3
E2
E1D1
C3
E4
B2
B3
disjoint
disjoint
Explanation
length
k + l = 5
Explanation
length
n + j = 6
n j k l
10
A is unsatisfiable
ODBASE 08 - Monterrey, México
Thursday, August 8, 13
KB is inconsistent
11
1. PhD ⊑ Student
2. disjoint(Professor, Student)
3. range(supervisedBy) ⊑ Professor
4. PhD(al)
5. supevisedBy(tim, al)
ODBASE 08 - Monterrey, México
Thursday, August 8, 13
KB is inconsistent
11
1. PhD ⊑ Student
2. disjoint(Professor, Student)
3. range(supervisedBy) ⊑ Professor
4. PhD(al)
5. supevisedBy(tim, al)
al is a Student
al is a Professor
no Student can be a Professor ➔	 2
ODBASE 08 - Monterrey, México
Thursday, August 8, 13
KB is inconsistent
11
1. PhD ⊑ Student
2. disjoint(Professor, Student)
3. range(supervisedBy) ⊑ Professor
4. PhD(al)
5. supevisedBy(tim, al)
al is a Student
!every PhD is also a Student ➔	 1
!al is a PhD ➔	 4
al is a Professor
no Student can be a Professor ➔	 2
ODBASE 08 - Monterrey, México
Thursday, August 8, 13
KB is inconsistent
11
1. PhD ⊑ Student
2. disjoint(Professor, Student)
3. range(supervisedBy) ⊑ Professor
4. PhD(al)
5. supevisedBy(tim, al)
al is a Student
!every PhD is also a Student ➔	 1
!al is a PhD ➔	 4
al is a Professor
everything in the range of supervisedBy is also
a Professor ➔	 3
al is in range of supervisedBy
no Student can be a Professor ➔	 2
ODBASE 08 - Monterrey, México
Thursday, August 8, 13
KB is inconsistent
11
1. PhD ⊑ Student
2. disjoint(Professor, Student)
3. range(supervisedBy) ⊑ Professor
4. PhD(al)
5. supevisedBy(tim, al)
al is a Student
!every PhD is also a Student ➔	 1
!al is a PhD ➔	 4
al is a Professor
everything in the range of supervisedBy is also
a Professor ➔	 3
al is in range of supervisedBy
tim supervisedBy al ➔	 5
no Student can be a Professor ➔	 2
ODBASE 08 - Monterrey, México
Thursday, August 8, 13
Query Answering
• Successful queries
12 ODBASE 08 - Monterrey, México
Thursday, August 8, 13
⋮
Student(bob)
Student(juan)
supervisedBy(tom, bob)
Student(al)
supervisedBy(tim, al)
teaches(al, ben)
teaches(sam, ben)
teaches(sam, john)
teaches(sam, karl)
⋮
CQs in regular DBs
q(x) :- Student(x), supervisedBy(y,x), teaches(x,z)
13 ODBASE 08 - Monterrey, México
Thursday, August 8, 13
⋮
Student(bob)
Student(juan)
supervisedBy(tom, bob)
Student(al)
supervisedBy(tim, al)
teaches(al, ben)
teaches(sam, ben)
teaches(sam, john)
teaches(sam, karl)
⋮
CQs in regular DBs
q(x) :- Student(x), supervisedBy(y,x), teaches(x,z)
q(al)
13 ODBASE 08 - Monterrey, México
Thursday, August 8, 13
⋮
Student(bob)
Student(juan)
supervisedBy(tom, bob)
Student(al)
supervisedBy(tim, al)
teaches(al, ben)
teaches(sam, ben)
teaches(sam, john)
teaches(sam, karl)
⋮
CQs in regular DBs
q(x) :- Student(x), supervisedBy(y,x), teaches(x,z)
q(al)
Student(al), x=al
supervisedBy(tim, al), y=tim
teaches(al, ben), z = ben
13 ODBASE 08 - Monterrey, México
Thursday, August 8, 13
1.PhD ⊑ Student
2.PhD ⊑ dom(supervisedBy)
3.range(supervisedBy) ⊑ Professor
4.Professor ⊑ domain(teaches)
5.PhD(al)
q(x) :- Student(x), supervisedBy(x,y),
teaches(y,z)
14 ODBASE 08 - Monterrey, México
Thursday, August 8, 13
1.PhD ⊑ Student
2.PhD ⊑ dom(supervisedBy)
3.range(supervisedBy) ⊑ Professor
4.Professor ⊑ domain(teaches)
5.PhD(al)
q(x) :- Student(x), supervisedBy(x,y),
teaches(y,z)
q(al)
14 ODBASE 08 - Monterrey, México
Thursday, August 8, 13
al supervisedBy @1!!
1.PhD ⊑ Student
2.PhD ⊑ dom(supervisedBy)
3.range(supervisedBy) ⊑ Professor
4.Professor ⊑ domain(teaches)
5.PhD(al)
@1 teaches @2
al is a Student
q(x) :- Student(x), supervisedBy(x,y),
teaches(y,z)
q(al)
14 ODBASE 08 - Monterrey, México
Thursday, August 8, 13
al supervisedBy @1!!
1.PhD ⊑ Student
2.PhD ⊑ dom(supervisedBy)
3.range(supervisedBy) ⊑ Professor
4.Professor ⊑ domain(teaches)
5.PhD(al)
@1 teaches @2
al is a Student
every PhD is also a Student ➔ 1
al is a PhD ➔	 5	 
q(x) :- Student(x), supervisedBy(x,y),
teaches(y,z)
q(al)
14 ODBASE 08 - Monterrey, México
Thursday, August 8, 13
al supervisedBy @1!!
al is in the domain of supervisedBy
1.PhD ⊑ Student
2.PhD ⊑ dom(supervisedBy)
3.range(supervisedBy) ⊑ Professor
4.Professor ⊑ domain(teaches)
5.PhD(al)
@1 teaches @2
al is a Student
every PhD is also a Student ➔ 1
al is a PhD ➔	 5	 
q(x) :- Student(x), supervisedBy(x,y),
teaches(y,z)
q(al)
14 ODBASE 08 - Monterrey, México
Thursday, August 8, 13
al supervisedBy @1!!
al is in the domain of supervisedBy
every PhD is also in the domain of supervisedBy ➔	 2
al is a PhD ➔	 5
1.PhD ⊑ Student
2.PhD ⊑ dom(supervisedBy)
3.range(supervisedBy) ⊑ Professor
4.Professor ⊑ domain(teaches)
5.PhD(al)
@1 teaches @2
al is a Student
every PhD is also a Student ➔ 1
al is a PhD ➔	 5	 
q(x) :- Student(x), supervisedBy(x,y),
teaches(y,z)
q(al)
14 ODBASE 08 - Monterrey, México
Thursday, August 8, 13
al supervisedBy @1!!
al is in the domain of supervisedBy
every PhD is also in the domain of supervisedBy ➔	 2
al is a PhD ➔	 5
1.PhD ⊑ Student
2.PhD ⊑ dom(supervisedBy)
3.range(supervisedBy) ⊑ Professor
4.Professor ⊑ domain(teaches)
5.PhD(al)
@1 teaches @2
every Professor is in the domain of teaches ➔	 4
@1 is a Professor
al is a Student
every PhD is also a Student ➔ 1
al is a PhD ➔	 5	 
q(x) :- Student(x), supervisedBy(x,y),
teaches(y,z)
q(al)
14 ODBASE 08 - Monterrey, México
Thursday, August 8, 13
al supervisedBy @1!!
al is in the domain of supervisedBy
every PhD is also in the domain of supervisedBy ➔	 2
al is a PhD ➔	 5
1.PhD ⊑ Student
2.PhD ⊑ dom(supervisedBy)
3.range(supervisedBy) ⊑ Professor
4.Professor ⊑ domain(teaches)
5.PhD(al)
@1 teaches @2
every Professor is in the domain of teaches ➔	 4
@1 is a Professor
@1 is in the range of supervisedBy
everything in the range of supervisedBy is a
Professor ➔	 3
al is a Student
every PhD is also a Student ➔ 1
al is a PhD ➔	 5	 
q(x) :- Student(x), supervisedBy(x,y),
teaches(y,z)
q(al)
14 ODBASE 08 - Monterrey, México
Thursday, August 8, 13
al supervisedBy @1!!
al is in the domain of supervisedBy
every PhD is also in the domain of supervisedBy ➔	 2
al is a PhD ➔	 5
1.PhD ⊑ Student
2.PhD ⊑ dom(supervisedBy)
3.range(supervisedBy) ⊑ Professor
4.Professor ⊑ domain(teaches)
5.PhD(al)
@1 teaches @2
every Professor is in the domain of teaches ➔	 4
@1 is a Professor
@1 is in the range of supervisedBy
everything in the range of supervisedBy is a
Professor ➔	 3
al is a Student
every PhD is also a Student ➔ 1
al is a PhD ➔	 5	 
q(x) :- Student(x), supervisedBy(x,y),
teaches(y,z)
q(al)
14 ODBASE 08 - Monterrey, México
Thursday, August 8, 13
Query Answering
15 ODBASE 08 - Monterrey, México
Thursday, August 8, 13
Query Answering
• Successful queries
• Failed queries
15 ODBASE 08 - Monterrey, México
Thursday, August 8, 13
Query Answering
• Successful queries
• Failed queries
• Due to missing information
15 ODBASE 08 - Monterrey, México
Thursday, August 8, 13
Query Answering
• Successful queries
• Failed queries
• Due to missing information
• Due to unsatisfiability
15 ODBASE 08 - Monterrey, México
Thursday, August 8, 13
1.PhD ⊑ Student
2.range(supervisedBy) ⊑ Professor
3.disjoint(Professor,Student)
q(x):- PhD(x), supervisedBy(y, x)
q inconsistent
16 ODBASE 08 - Monterrey, México
Thursday, August 8, 13
@1 is a Professor
@1 is a Student
1.PhD ⊑ Student
2.range(supervisedBy) ⊑ Professor
3.disjoint(Professor,Student)
q(x):- PhD(x), supervisedBy(y, x)
q inconsistent
q(@1) :- PhD(@1), supervisedBy(@2, @1)
no Student can be a Professor ➔	 3
16 ODBASE 08 - Monterrey, México
Thursday, August 8, 13
@1 is a Professor
@1 is a Student
@1 is a PhD
every PhD is also a Student ➔	 1
1.PhD ⊑ Student
2.range(supervisedBy) ⊑ Professor
3.disjoint(Professor,Student)
q(x):- PhD(x), supervisedBy(y, x)
q inconsistent
q(@1) :- PhD(@1), supervisedBy(@2, @1)
no Student can be a Professor ➔	 3
16 ODBASE 08 - Monterrey, México
Thursday, August 8, 13
@1 is a Professor
@1 is a Student
@1 is a PhD
every PhD is also a Student ➔	 1
1.PhD ⊑ Student
2.range(supervisedBy) ⊑ Professor
3.disjoint(Professor,Student)
q(x):- PhD(x), supervisedBy(y, x)
q inconsistent
q(@1) :- PhD(@1), supervisedBy(@2, @1)
no Student can be a Professor ➔	 3
16 ODBASE 08 - Monterrey, México
Thursday, August 8, 13
@1 is a Professor
@2 supervisedBy @1
everything in the range of supervisedBy is a Professor ➔ 2
@1 is a Student
@1 is a PhD
every PhD is also a Student ➔	 1
1.PhD ⊑ Student
2.range(supervisedBy) ⊑ Professor
3.disjoint(Professor,Student)
q(x):- PhD(x), supervisedBy(y, x)
q inconsistent
q(@1) :- PhD(@1), supervisedBy(@2, @1)
no Student can be a Professor ➔	 3
16 ODBASE 08 - Monterrey, México
Thursday, August 8, 13
@1 is a Professor
@2 supervisedBy @1
everything in the range of supervisedBy is a Professor ➔ 2
@1 is a Student
@1 is a PhD
every PhD is also a Student ➔	 1
1.PhD ⊑ Student
2.range(supervisedBy) ⊑ Professor
3.disjoint(Professor,Student)
q(x):- PhD(x), supervisedBy(y, x)
q inconsistent
q(@1) :- PhD(@1), supervisedBy(@2, @1)
no Student can be a Professor ➔	 3
16 ODBASE 08 - Monterrey, México
Thursday, August 8, 13
Conclusions
• We addressed DL-Lite explanations can be given for
traditional reasoning services (shortness of proof).
• Finite model case.
• Explaining conjunctive queries when reasoning is
present by a) exploiting existing DL-Lite query
rewriting algorithm and b) Prolog-based program.
• We looked into the problem of explaining failed
answers to queries.
17 ODBASE 08 - Monterrey, México
Thursday, August 8, 13
Future Work
• Explanation of failed answers to CQs over
DL-Lite ontologies.
• Integration of prototype with the QuOnto
Reasoner.
• Explanation in Ontology Based Data Access
(presence of mappings).
• Field testing of the algorithms.
18 ODBASE 08 - Monterrey, México
Thursday, August 8, 13

Más contenido relacionado

Similar a ODBASE'08 dl-lite explanations

Approaches for the Integration of Visual and Computational Analysis of Biomed...
Approaches for the Integration of Visual and Computational Analysis of Biomed...Approaches for the Integration of Visual and Computational Analysis of Biomed...
Approaches for the Integration of Visual and Computational Analysis of Biomed...Nils Gehlenborg
 
Automatic extraction of bioactivity data from patents
Automatic extraction of bioactivity data from patentsAutomatic extraction of bioactivity data from patents
Automatic extraction of bioactivity data from patentsNextMove Software
 
Analyze an Argument_ Practice 1 (English I Reading) _ Texas Gateway.pdf
Analyze an Argument_ Practice 1 (English I Reading) _ Texas Gateway.pdfAnalyze an Argument_ Practice 1 (English I Reading) _ Texas Gateway.pdf
Analyze an Argument_ Practice 1 (English I Reading) _ Texas Gateway.pdfrrrrrrrrr4
 
Linked data in libraries: another fad or paradigm shift?
Linked data in libraries: another fad or paradigm shift?Linked data in libraries: another fad or paradigm shift?
Linked data in libraries: another fad or paradigm shift?Amber Billey
 
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 dependenciesMariano Rodriguez-Muro
 
Revealing Entities From Texts With a Hybrid Approach
Revealing Entities From Texts With a Hybrid ApproachRevealing Entities From Texts With a Hybrid Approach
Revealing Entities From Texts With a Hybrid ApproachJulien PLU
 
A Practical Ontology for the Large-Scale Modeling of Scholarly Artifacts and ...
A Practical Ontology for the Large-Scale Modeling of Scholarly Artifacts and ...A Practical Ontology for the Large-Scale Modeling of Scholarly Artifacts and ...
A Practical Ontology for the Large-Scale Modeling of Scholarly Artifacts and ...Marko Rodriguez
 
Sharing Data Sets to Personalize Learning
Sharing Data Sets to Personalize LearningSharing Data Sets to Personalize Learning
Sharing Data Sets to Personalize LearningHendrik Drachsler
 
Propagation of Policies in Rich Data Flows
Propagation of Policies in Rich Data FlowsPropagation of Policies in Rich Data Flows
Propagation of Policies in Rich Data FlowsEnrico Daga
 
Constructing Semantic Gazetteers: Managing GeoSpatial Vocabularies Using Open...
Constructing Semantic Gazetteers: Managing GeoSpatial Vocabularies Using Open...Constructing Semantic Gazetteers: Managing GeoSpatial Vocabularies Using Open...
Constructing Semantic Gazetteers: Managing GeoSpatial Vocabularies Using Open...Stephane Fellah
 
Studi Penerapan Ontologi dalam Bahasa Inggris sebagai Kerangka
Studi Penerapan Ontologi dalam Bahasa Inggris sebagai KerangkaStudi Penerapan Ontologi dalam Bahasa Inggris sebagai Kerangka
Studi Penerapan Ontologi dalam Bahasa Inggris sebagai KerangkaMetilova Sitorus
 

Similar a ODBASE'08 dl-lite explanations (14)

DL'12 dl-lite explanations
DL'12 dl-lite explanationsDL'12 dl-lite explanations
DL'12 dl-lite explanations
 
Approaches for the Integration of Visual and Computational Analysis of Biomed...
Approaches for the Integration of Visual and Computational Analysis of Biomed...Approaches for the Integration of Visual and Computational Analysis of Biomed...
Approaches for the Integration of Visual and Computational Analysis of Biomed...
 
Automatic extraction of bioactivity data from patents
Automatic extraction of bioactivity data from patentsAutomatic extraction of bioactivity data from patents
Automatic extraction of bioactivity data from patents
 
Analyze an Argument_ Practice 1 (English I Reading) _ Texas Gateway.pdf
Analyze an Argument_ Practice 1 (English I Reading) _ Texas Gateway.pdfAnalyze an Argument_ Practice 1 (English I Reading) _ Texas Gateway.pdf
Analyze an Argument_ Practice 1 (English I Reading) _ Texas Gateway.pdf
 
Loupe model - Use Cases and Requirements
Loupe model - Use Cases and Requirements Loupe model - Use Cases and Requirements
Loupe model - Use Cases and Requirements
 
Linked data in libraries: another fad or paradigm shift?
Linked data in libraries: another fad or paradigm shift?Linked data in libraries: another fad or paradigm shift?
Linked data in libraries: another fad or paradigm shift?
 
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
 
Revealing Entities From Texts With a Hybrid Approach
Revealing Entities From Texts With a Hybrid ApproachRevealing Entities From Texts With a Hybrid Approach
Revealing Entities From Texts With a Hybrid Approach
 
A Practical Ontology for the Large-Scale Modeling of Scholarly Artifacts and ...
A Practical Ontology for the Large-Scale Modeling of Scholarly Artifacts and ...A Practical Ontology for the Large-Scale Modeling of Scholarly Artifacts and ...
A Practical Ontology for the Large-Scale Modeling of Scholarly Artifacts and ...
 
Sharing Data Sets to Personalize Learning
Sharing Data Sets to Personalize LearningSharing Data Sets to Personalize Learning
Sharing Data Sets to Personalize Learning
 
Propagation of Policies in Rich Data Flows
Propagation of Policies in Rich Data FlowsPropagation of Policies in Rich Data Flows
Propagation of Policies in Rich Data Flows
 
Constructing Semantic Gazetteers: Managing GeoSpatial Vocabularies Using Open...
Constructing Semantic Gazetteers: Managing GeoSpatial Vocabularies Using Open...Constructing Semantic Gazetteers: Managing GeoSpatial Vocabularies Using Open...
Constructing Semantic Gazetteers: Managing GeoSpatial Vocabularies Using Open...
 
Studi Penerapan Ontologi dalam Bahasa Inggris sebagai Kerangka
Studi Penerapan Ontologi dalam Bahasa Inggris sebagai KerangkaStudi Penerapan Ontologi dalam Bahasa Inggris sebagai Kerangka
Studi Penerapan Ontologi dalam Bahasa Inggris sebagai Kerangka
 
CV_Ind
CV_IndCV_Ind
CV_Ind
 

Más de Mariano Rodriguez-Muro

SWT Lecture Session 9 - RDB2RDF direct mapping
SWT Lecture Session 9 - RDB2RDF direct mappingSWT Lecture Session 9 - RDB2RDF direct mapping
SWT Lecture Session 9 - RDB2RDF direct mappingMariano Rodriguez-Muro
 
SWT Lecture Session 8 - Inference in jena
SWT Lecture Session 8 - Inference in jenaSWT Lecture Session 8 - Inference in jena
SWT Lecture Session 8 - Inference in jenaMariano Rodriguez-Muro
 
SWT Lecture Session 7 - Advanced uses of RDFS
SWT Lecture Session 7 - Advanced uses of RDFSSWT Lecture Session 7 - Advanced uses of RDFS
SWT Lecture Session 7 - Advanced uses of RDFSMariano Rodriguez-Muro
 
SWT Lecture Session 6 - RDFS semantics, inference techniques, sesame rdfs
SWT Lecture Session 6 - RDFS semantics, inference techniques, sesame rdfsSWT Lecture Session 6 - RDFS semantics, inference techniques, sesame rdfs
SWT Lecture Session 6 - RDFS semantics, inference techniques, sesame rdfsMariano Rodriguez-Muro
 
SWT Lecture Session 4 - SW architectures and SPARQL
SWT Lecture Session 4 - SW architectures and SPARQLSWT Lecture Session 4 - SW architectures and SPARQL
SWT Lecture Session 4 - SW architectures and SPARQLMariano Rodriguez-Muro
 

Más de Mariano Rodriguez-Muro (20)

SWT Lecture Session 2 - RDF
SWT Lecture Session 2 - RDFSWT Lecture Session 2 - RDF
SWT Lecture Session 2 - RDF
 
SWT Lab 3
SWT Lab 3SWT Lab 3
SWT Lab 3
 
SWT Lab 5
SWT Lab 5SWT Lab 5
SWT Lab 5
 
SWT Lab 2
SWT Lab 2SWT Lab 2
SWT Lab 2
 
SWT Lab 1
SWT Lab 1SWT Lab 1
SWT Lab 1
 
SWT Lecture Session 11 - R2RML part 2
SWT Lecture Session 11 - R2RML part 2SWT Lecture Session 11 - R2RML part 2
SWT Lecture Session 11 - R2RML part 2
 
SWT Lecture Session 10 R2RML Part 1
SWT Lecture Session 10 R2RML Part 1SWT Lecture Session 10 R2RML Part 1
SWT Lecture Session 10 R2RML Part 1
 
SWT Lecture Session 9 - RDB2RDF direct mapping
SWT Lecture Session 9 - RDB2RDF direct mappingSWT Lecture Session 9 - RDB2RDF direct mapping
SWT Lecture Session 9 - RDB2RDF direct mapping
 
SWT Lecture Session 8 - Rules
SWT Lecture Session 8 - RulesSWT Lecture Session 8 - Rules
SWT Lecture Session 8 - Rules
 
SWT Lecture Session 8 - Inference in jena
SWT Lecture Session 8 - Inference in jenaSWT Lecture Session 8 - Inference in jena
SWT Lecture Session 8 - Inference in jena
 
SWT Lecture Session 7 - Advanced uses of RDFS
SWT Lecture Session 7 - Advanced uses of RDFSSWT Lecture Session 7 - Advanced uses of RDFS
SWT Lecture Session 7 - Advanced uses of RDFS
 
SWT Lecture Session 6 - RDFS semantics, inference techniques, sesame rdfs
SWT Lecture Session 6 - RDFS semantics, inference techniques, sesame rdfsSWT Lecture Session 6 - RDFS semantics, inference techniques, sesame rdfs
SWT Lecture Session 6 - RDFS semantics, inference techniques, sesame rdfs
 
SWT Lecture Session 5 - RDFS
SWT Lecture Session 5 - RDFSSWT Lecture Session 5 - RDFS
SWT Lecture Session 5 - RDFS
 
SWT Lecture Session 4 - SW architectures and SPARQL
SWT Lecture Session 4 - SW architectures and SPARQLSWT Lecture Session 4 - SW architectures and SPARQL
SWT Lecture Session 4 - SW architectures and SPARQL
 
SWT Lecture Session 4 - Sesame
SWT Lecture Session 4 - SesameSWT Lecture Session 4 - Sesame
SWT Lecture Session 4 - Sesame
 
SWT Lecture Session 3 - SPARQL
SWT Lecture Session 3 - SPARQLSWT Lecture Session 3 - SPARQL
SWT Lecture Session 3 - SPARQL
 
7 advanced uses of rdfs
7 advanced uses of rdfs7 advanced uses of rdfs
7 advanced uses of rdfs
 
5 rdfs
5 rdfs5 rdfs
5 rdfs
 
4 sw architectures and sparql
4 sw architectures and sparql4 sw architectures and sparql
4 sw architectures and sparql
 
4 sesame
4 sesame4 sesame
4 sesame
 

Último

The Future of Software Development - Devin AI Innovative Approach.pdf
The Future of Software Development - Devin AI Innovative Approach.pdfThe Future of Software Development - Devin AI Innovative Approach.pdf
The Future of Software Development - Devin AI Innovative Approach.pdfSeasiaInfotech2
 
Human Factors of XR: Using Human Factors to Design XR Systems
Human Factors of XR: Using Human Factors to Design XR SystemsHuman Factors of XR: Using Human Factors to Design XR Systems
Human Factors of XR: Using Human Factors to Design XR SystemsMark Billinghurst
 
Ensuring Technical Readiness For Copilot in Microsoft 365
Ensuring Technical Readiness For Copilot in Microsoft 365Ensuring Technical Readiness For Copilot in Microsoft 365
Ensuring Technical Readiness For Copilot in Microsoft 3652toLead Limited
 
Transcript: New from BookNet Canada for 2024: BNC CataList - Tech Forum 2024
Transcript: New from BookNet Canada for 2024: BNC CataList - Tech Forum 2024Transcript: New from BookNet Canada for 2024: BNC CataList - Tech Forum 2024
Transcript: New from BookNet Canada for 2024: BNC CataList - Tech Forum 2024BookNet Canada
 
Gen AI in Business - Global Trends Report 2024.pdf
Gen AI in Business - Global Trends Report 2024.pdfGen AI in Business - Global Trends Report 2024.pdf
Gen AI in Business - Global Trends Report 2024.pdfAddepto
 
Streamlining Python Development: A Guide to a Modern Project Setup
Streamlining Python Development: A Guide to a Modern Project SetupStreamlining Python Development: A Guide to a Modern Project Setup
Streamlining Python Development: A Guide to a Modern Project SetupFlorian Wilhelm
 
Kotlin Multiplatform & Compose Multiplatform - Starter kit for pragmatics
Kotlin Multiplatform & Compose Multiplatform - Starter kit for pragmaticsKotlin Multiplatform & Compose Multiplatform - Starter kit for pragmatics
Kotlin Multiplatform & Compose Multiplatform - Starter kit for pragmaticscarlostorres15106
 
Unleash Your Potential - Namagunga Girls Coding Club
Unleash Your Potential - Namagunga Girls Coding ClubUnleash Your Potential - Namagunga Girls Coding Club
Unleash Your Potential - Namagunga Girls Coding ClubKalema Edgar
 
Artificial intelligence in cctv survelliance.pptx
Artificial intelligence in cctv survelliance.pptxArtificial intelligence in cctv survelliance.pptx
Artificial intelligence in cctv survelliance.pptxhariprasad279825
 
Story boards and shot lists for my a level piece
Story boards and shot lists for my a level pieceStory boards and shot lists for my a level piece
Story boards and shot lists for my a level piececharlottematthew16
 
WordPress Websites for Engineers: Elevate Your Brand
WordPress Websites for Engineers: Elevate Your BrandWordPress Websites for Engineers: Elevate Your Brand
WordPress Websites for Engineers: Elevate Your Brandgvaughan
 
What's New in Teams Calling, Meetings and Devices March 2024
What's New in Teams Calling, Meetings and Devices March 2024What's New in Teams Calling, Meetings and Devices March 2024
What's New in Teams Calling, Meetings and Devices March 2024Stephanie Beckett
 
Are Multi-Cloud and Serverless Good or Bad?
Are Multi-Cloud and Serverless Good or Bad?Are Multi-Cloud and Serverless Good or Bad?
Are Multi-Cloud and Serverless Good or Bad?Mattias Andersson
 
Unraveling Multimodality with Large Language Models.pdf
Unraveling Multimodality with Large Language Models.pdfUnraveling Multimodality with Large Language Models.pdf
Unraveling Multimodality with Large Language Models.pdfAlex Barbosa Coqueiro
 
Leverage Zilliz Serverless - Up to 50X Saving for Your Vector Storage Cost
Leverage Zilliz Serverless - Up to 50X Saving for Your Vector Storage CostLeverage Zilliz Serverless - Up to 50X Saving for Your Vector Storage Cost
Leverage Zilliz Serverless - Up to 50X Saving for Your Vector Storage CostZilliz
 
"Subclassing and Composition – A Pythonic Tour of Trade-Offs", Hynek Schlawack
"Subclassing and Composition – A Pythonic Tour of Trade-Offs", Hynek Schlawack"Subclassing and Composition – A Pythonic Tour of Trade-Offs", Hynek Schlawack
"Subclassing and Composition – A Pythonic Tour of Trade-Offs", Hynek SchlawackFwdays
 
New from BookNet Canada for 2024: BNC CataList - Tech Forum 2024
New from BookNet Canada for 2024: BNC CataList - Tech Forum 2024New from BookNet Canada for 2024: BNC CataList - Tech Forum 2024
New from BookNet Canada for 2024: BNC CataList - Tech Forum 2024BookNet Canada
 
Developer Data Modeling Mistakes: From Postgres to NoSQL
Developer Data Modeling Mistakes: From Postgres to NoSQLDeveloper Data Modeling Mistakes: From Postgres to NoSQL
Developer Data Modeling Mistakes: From Postgres to NoSQLScyllaDB
 
Vector Databases 101 - An introduction to the world of Vector Databases
Vector Databases 101 - An introduction to the world of Vector DatabasesVector Databases 101 - An introduction to the world of Vector Databases
Vector Databases 101 - An introduction to the world of Vector DatabasesZilliz
 

Último (20)

The Future of Software Development - Devin AI Innovative Approach.pdf
The Future of Software Development - Devin AI Innovative Approach.pdfThe Future of Software Development - Devin AI Innovative Approach.pdf
The Future of Software Development - Devin AI Innovative Approach.pdf
 
Human Factors of XR: Using Human Factors to Design XR Systems
Human Factors of XR: Using Human Factors to Design XR SystemsHuman Factors of XR: Using Human Factors to Design XR Systems
Human Factors of XR: Using Human Factors to Design XR Systems
 
Ensuring Technical Readiness For Copilot in Microsoft 365
Ensuring Technical Readiness For Copilot in Microsoft 365Ensuring Technical Readiness For Copilot in Microsoft 365
Ensuring Technical Readiness For Copilot in Microsoft 365
 
Transcript: New from BookNet Canada for 2024: BNC CataList - Tech Forum 2024
Transcript: New from BookNet Canada for 2024: BNC CataList - Tech Forum 2024Transcript: New from BookNet Canada for 2024: BNC CataList - Tech Forum 2024
Transcript: New from BookNet Canada for 2024: BNC CataList - Tech Forum 2024
 
Gen AI in Business - Global Trends Report 2024.pdf
Gen AI in Business - Global Trends Report 2024.pdfGen AI in Business - Global Trends Report 2024.pdf
Gen AI in Business - Global Trends Report 2024.pdf
 
Streamlining Python Development: A Guide to a Modern Project Setup
Streamlining Python Development: A Guide to a Modern Project SetupStreamlining Python Development: A Guide to a Modern Project Setup
Streamlining Python Development: A Guide to a Modern Project Setup
 
Kotlin Multiplatform & Compose Multiplatform - Starter kit for pragmatics
Kotlin Multiplatform & Compose Multiplatform - Starter kit for pragmaticsKotlin Multiplatform & Compose Multiplatform - Starter kit for pragmatics
Kotlin Multiplatform & Compose Multiplatform - Starter kit for pragmatics
 
Unleash Your Potential - Namagunga Girls Coding Club
Unleash Your Potential - Namagunga Girls Coding ClubUnleash Your Potential - Namagunga Girls Coding Club
Unleash Your Potential - Namagunga Girls Coding Club
 
Artificial intelligence in cctv survelliance.pptx
Artificial intelligence in cctv survelliance.pptxArtificial intelligence in cctv survelliance.pptx
Artificial intelligence in cctv survelliance.pptx
 
Story boards and shot lists for my a level piece
Story boards and shot lists for my a level pieceStory boards and shot lists for my a level piece
Story boards and shot lists for my a level piece
 
WordPress Websites for Engineers: Elevate Your Brand
WordPress Websites for Engineers: Elevate Your BrandWordPress Websites for Engineers: Elevate Your Brand
WordPress Websites for Engineers: Elevate Your Brand
 
What's New in Teams Calling, Meetings and Devices March 2024
What's New in Teams Calling, Meetings and Devices March 2024What's New in Teams Calling, Meetings and Devices March 2024
What's New in Teams Calling, Meetings and Devices March 2024
 
E-Vehicle_Hacking_by_Parul Sharma_null_owasp.pptx
E-Vehicle_Hacking_by_Parul Sharma_null_owasp.pptxE-Vehicle_Hacking_by_Parul Sharma_null_owasp.pptx
E-Vehicle_Hacking_by_Parul Sharma_null_owasp.pptx
 
Are Multi-Cloud and Serverless Good or Bad?
Are Multi-Cloud and Serverless Good or Bad?Are Multi-Cloud and Serverless Good or Bad?
Are Multi-Cloud and Serverless Good or Bad?
 
Unraveling Multimodality with Large Language Models.pdf
Unraveling Multimodality with Large Language Models.pdfUnraveling Multimodality with Large Language Models.pdf
Unraveling Multimodality with Large Language Models.pdf
 
Leverage Zilliz Serverless - Up to 50X Saving for Your Vector Storage Cost
Leverage Zilliz Serverless - Up to 50X Saving for Your Vector Storage CostLeverage Zilliz Serverless - Up to 50X Saving for Your Vector Storage Cost
Leverage Zilliz Serverless - Up to 50X Saving for Your Vector Storage Cost
 
"Subclassing and Composition – A Pythonic Tour of Trade-Offs", Hynek Schlawack
"Subclassing and Composition – A Pythonic Tour of Trade-Offs", Hynek Schlawack"Subclassing and Composition – A Pythonic Tour of Trade-Offs", Hynek Schlawack
"Subclassing and Composition – A Pythonic Tour of Trade-Offs", Hynek Schlawack
 
New from BookNet Canada for 2024: BNC CataList - Tech Forum 2024
New from BookNet Canada for 2024: BNC CataList - Tech Forum 2024New from BookNet Canada for 2024: BNC CataList - Tech Forum 2024
New from BookNet Canada for 2024: BNC CataList - Tech Forum 2024
 
Developer Data Modeling Mistakes: From Postgres to NoSQL
Developer Data Modeling Mistakes: From Postgres to NoSQLDeveloper Data Modeling Mistakes: From Postgres to NoSQL
Developer Data Modeling Mistakes: From Postgres to NoSQL
 
Vector Databases 101 - An introduction to the world of Vector Databases
Vector Databases 101 - An introduction to the world of Vector DatabasesVector Databases 101 - An introduction to the world of Vector Databases
Vector Databases 101 - An introduction to the world of Vector Databases
 

ODBASE'08 dl-lite explanations

  • 1. Explanations for DL-Lite Alexander Borgida 2 Diego Calvanese 1 Mariano Rodríguez-Muro 1 1 1 Free University of Bozen Bolzano 2 Rutgers University ODBASE 08 - Monterrey, México Thursday, August 8, 13
  • 2. Outline • Explanations • DL-Lite and Explanations for DL-Lite • Explanations for traditional services • Explanations for conjunctive queries • Conclusions and future work 2 ODBASE 08 - Monterrey, México Thursday, August 8, 13
  • 3. Explanations 3 ODBASE 08 - Monterrey, México Thursday, August 8, 13
  • 4. Explanations Why explanations? 3 ODBASE 08 - Monterrey, México Thursday, August 8, 13
  • 5. Explanations Why explanations? What are explanations? 3 ODBASE 08 - Monterrey, México Thursday, August 8, 13
  • 6. Explanations Why explanations? What are explanations? • Explanations are formal proofs, constructed from premises using rules of inference. 3 ODBASE 08 - Monterrey, México Thursday, August 8, 13
  • 7. Explanations Why explanations? What are explanations? • Explanations are formal proofs, constructed from premises using rules of inference. 3 Features of an explanation: ODBASE 08 - Monterrey, México Thursday, August 8, 13
  • 8. Explanations Why explanations? What are explanations? • Explanations are formal proofs, constructed from premises using rules of inference. 3 Features of an explanation: • Style • Length • Presentation ODBASE 08 - Monterrey, México Thursday, August 8, 13
  • 9. Explanations (cont.) 4 ODBASE 08 - Monterrey, México Thursday, August 8, 13
  • 10. Explanations (cont.) Audience: KB developer, End user 4 ODBASE 08 - Monterrey, México Thursday, August 8, 13
  • 11. Explanations (cont.) Audience: KB developer, End user 4 1. Style. Understandable inference rules (NOT refutation or resolution) ODBASE 08 - Monterrey, México Thursday, August 8, 13
  • 12. Explanations (cont.) Audience: KB developer, End user 4 1. Style. Understandable inference rules (NOT refutation or resolution) 2. Length: 'shorter' preferred ODBASE 08 - Monterrey, México Thursday, August 8, 13
  • 13. Explanations (cont.) Audience: KB developer, End user 4 1. Style. Understandable inference rules (NOT refutation or resolution) 2. Length: 'shorter' preferred 3. Presentation: Complete proof vs Iterative process. as indicated by the user. Possibly eliminating 'obvious' parts (not addressed here) ODBASE 08 - Monterrey, México Thursday, August 8, 13
  • 14. DL-Litef Concept constructs B ::= A | ƎP | ƎP- C::= B | ¬B | C1 ⊓ C2 TBox Assertions B ⊑ C (funct P) (funt P-) ABox Assertions A(a) R(a,b) 5 ODBASE 08 - Monterrey, México Thursday, August 8, 13
  • 15. DL-Litef Is-A Hierarchies Class disjointness Role-typing Participation constraints Non-participation constraints Functionality restrictions A1 ⊑ A2 A1 ⊑ ¬A2 ƎP ⊑ A1 ƎP- ⊑ A1 A1 ⊑ ƎP A1 ⊑ ƎP- A1 ⊑ ¬ƎP A1 ⊑ ¬ƎP- (funct P) (funct P-) 6 ODBASE 08 - Monterrey, México Thursday, August 8, 13
  • 16. DL-Litef A1 ⊑ A2 A1 ⊑ ¬A2 ƎP ⊑ A1 ƎP- ⊑ A1 A1 ⊑ ƎP A1 ⊑ ƎP- A1 ⊑ ¬ƎP A1 ⊑ ¬ƎP- (funct P) (funct P-) A1 ⊑ A2 disjoint(A1,A2) domain(P) ⊑ A1 range(P) ⊑ A1 A1 ⊑ domain(P) A1 ⊑ range(P) disjoint(A1, domain(P)) disjoint(A1, range(P)) (funct P) (funct P-) 6 ODBASE 08 - Monterrey, México Thursday, August 8, 13
  • 17. Reasoning services 7 ODBASE 08 - Monterrey, México Thursday, August 8, 13
  • 18. Reasoning services • Standard Inferences • TBox reasoning (concept consistency, subsumption) • ABox reasoning (KB satisfiability, Instance checking*) 7 ODBASE 08 - Monterrey, México Thursday, August 8, 13
  • 19. Reasoning services • Standard Inferences • TBox reasoning (concept consistency, subsumption) • ABox reasoning (KB satisfiability, Instance checking*) ✦ Finite model reasoning 7 ODBASE 08 - Monterrey, México Thursday, August 8, 13
  • 20. Reasoning services • Standard Inferences • TBox reasoning (concept consistency, subsumption) • ABox reasoning (KB satisfiability, Instance checking*) ✦ Finite model reasoning • Conjunctive Query Answering 7 ODBASE 08 - Monterrey, México Thursday, August 8, 13
  • 21. Reasoning services • Standard Inferences • TBox reasoning (concept consistency, subsumption) • ABox reasoning (KB satisfiability, Instance checking*) ✦ Finite model reasoning • Conjunctive Query Answering • Successful queries • Failed queries 7 ODBASE 08 - Monterrey, México Thursday, August 8, 13
  • 22. B C A D Concept Subsumption 8 ODBASE 08 - Monterrey, México Thursday, August 8, 13
  • 23. B C A D Concept Subsumption • Hierarchy Traversing 8 ODBASE 08 - Monterrey, México Thursday, August 8, 13
  • 24. B C A D Concept Subsumption • Hierarchy Traversing A ⊑ D ⊓ C 8 ODBASE 08 - Monterrey, México Thursday, August 8, 13
  • 25. B C A D Concept Subsumption • Hierarchy Traversing A ⊑ D ⊓ C A ⊑ C A ⊑ B B ⊑ D 8 ODBASE 08 - Monterrey, México Thursday, August 8, 13
  • 26. B C A D Concept Subsumption • Hierarchy Traversing A ⊑ D ⊓ C A ⊑ C A ⊑ B B ⊑ D • Minimal size explanations 8 ODBASE 08 - Monterrey, México Thursday, August 8, 13
  • 27. B1 C2 C1 A C3 B2 B3 disjoint n j 9 A is unsatisfiable ODBASE 08 - Monterrey, México Thursday, August 8, 13
  • 28. B1 C2 C1 A C3 B2 B3 disjoint n j 9 Explanation length n + j = 6 A is unsatisfiable ODBASE 08 - Monterrey, México Thursday, August 8, 13
  • 29. B1 C2 C1 A E3 E2 E1D1 C3 E4 B2 B3 disjoint disjoint n j k l 10 A is unsatisfiable ODBASE 08 - Monterrey, México Thursday, August 8, 13
  • 30. B1 C2 C1 A E3 E2 E1D1 C3 E4 B2 B3 disjoint disjoint Explanation length k + l = 5 Explanation length n + j = 6 n j k l 10 A is unsatisfiable ODBASE 08 - Monterrey, México Thursday, August 8, 13
  • 31. KB is inconsistent 11 1. PhD ⊑ Student 2. disjoint(Professor, Student) 3. range(supervisedBy) ⊑ Professor 4. PhD(al) 5. supevisedBy(tim, al) ODBASE 08 - Monterrey, México Thursday, August 8, 13
  • 32. KB is inconsistent 11 1. PhD ⊑ Student 2. disjoint(Professor, Student) 3. range(supervisedBy) ⊑ Professor 4. PhD(al) 5. supevisedBy(tim, al) al is a Student al is a Professor no Student can be a Professor ➔ 2 ODBASE 08 - Monterrey, México Thursday, August 8, 13
  • 33. KB is inconsistent 11 1. PhD ⊑ Student 2. disjoint(Professor, Student) 3. range(supervisedBy) ⊑ Professor 4. PhD(al) 5. supevisedBy(tim, al) al is a Student !every PhD is also a Student ➔ 1 !al is a PhD ➔ 4 al is a Professor no Student can be a Professor ➔ 2 ODBASE 08 - Monterrey, México Thursday, August 8, 13
  • 34. KB is inconsistent 11 1. PhD ⊑ Student 2. disjoint(Professor, Student) 3. range(supervisedBy) ⊑ Professor 4. PhD(al) 5. supevisedBy(tim, al) al is a Student !every PhD is also a Student ➔ 1 !al is a PhD ➔ 4 al is a Professor everything in the range of supervisedBy is also a Professor ➔ 3 al is in range of supervisedBy no Student can be a Professor ➔ 2 ODBASE 08 - Monterrey, México Thursday, August 8, 13
  • 35. KB is inconsistent 11 1. PhD ⊑ Student 2. disjoint(Professor, Student) 3. range(supervisedBy) ⊑ Professor 4. PhD(al) 5. supevisedBy(tim, al) al is a Student !every PhD is also a Student ➔ 1 !al is a PhD ➔ 4 al is a Professor everything in the range of supervisedBy is also a Professor ➔ 3 al is in range of supervisedBy tim supervisedBy al ➔ 5 no Student can be a Professor ➔ 2 ODBASE 08 - Monterrey, México Thursday, August 8, 13
  • 36. Query Answering • Successful queries 12 ODBASE 08 - Monterrey, México Thursday, August 8, 13
  • 37. ⋮ Student(bob) Student(juan) supervisedBy(tom, bob) Student(al) supervisedBy(tim, al) teaches(al, ben) teaches(sam, ben) teaches(sam, john) teaches(sam, karl) ⋮ CQs in regular DBs q(x) :- Student(x), supervisedBy(y,x), teaches(x,z) 13 ODBASE 08 - Monterrey, México Thursday, August 8, 13
  • 38. ⋮ Student(bob) Student(juan) supervisedBy(tom, bob) Student(al) supervisedBy(tim, al) teaches(al, ben) teaches(sam, ben) teaches(sam, john) teaches(sam, karl) ⋮ CQs in regular DBs q(x) :- Student(x), supervisedBy(y,x), teaches(x,z) q(al) 13 ODBASE 08 - Monterrey, México Thursday, August 8, 13
  • 39. ⋮ Student(bob) Student(juan) supervisedBy(tom, bob) Student(al) supervisedBy(tim, al) teaches(al, ben) teaches(sam, ben) teaches(sam, john) teaches(sam, karl) ⋮ CQs in regular DBs q(x) :- Student(x), supervisedBy(y,x), teaches(x,z) q(al) Student(al), x=al supervisedBy(tim, al), y=tim teaches(al, ben), z = ben 13 ODBASE 08 - Monterrey, México Thursday, August 8, 13
  • 40. 1.PhD ⊑ Student 2.PhD ⊑ dom(supervisedBy) 3.range(supervisedBy) ⊑ Professor 4.Professor ⊑ domain(teaches) 5.PhD(al) q(x) :- Student(x), supervisedBy(x,y), teaches(y,z) 14 ODBASE 08 - Monterrey, México Thursday, August 8, 13
  • 41. 1.PhD ⊑ Student 2.PhD ⊑ dom(supervisedBy) 3.range(supervisedBy) ⊑ Professor 4.Professor ⊑ domain(teaches) 5.PhD(al) q(x) :- Student(x), supervisedBy(x,y), teaches(y,z) q(al) 14 ODBASE 08 - Monterrey, México Thursday, August 8, 13
  • 42. al supervisedBy @1!! 1.PhD ⊑ Student 2.PhD ⊑ dom(supervisedBy) 3.range(supervisedBy) ⊑ Professor 4.Professor ⊑ domain(teaches) 5.PhD(al) @1 teaches @2 al is a Student q(x) :- Student(x), supervisedBy(x,y), teaches(y,z) q(al) 14 ODBASE 08 - Monterrey, México Thursday, August 8, 13
  • 43. al supervisedBy @1!! 1.PhD ⊑ Student 2.PhD ⊑ dom(supervisedBy) 3.range(supervisedBy) ⊑ Professor 4.Professor ⊑ domain(teaches) 5.PhD(al) @1 teaches @2 al is a Student every PhD is also a Student ➔ 1 al is a PhD ➔ 5 q(x) :- Student(x), supervisedBy(x,y), teaches(y,z) q(al) 14 ODBASE 08 - Monterrey, México Thursday, August 8, 13
  • 44. al supervisedBy @1!! al is in the domain of supervisedBy 1.PhD ⊑ Student 2.PhD ⊑ dom(supervisedBy) 3.range(supervisedBy) ⊑ Professor 4.Professor ⊑ domain(teaches) 5.PhD(al) @1 teaches @2 al is a Student every PhD is also a Student ➔ 1 al is a PhD ➔ 5 q(x) :- Student(x), supervisedBy(x,y), teaches(y,z) q(al) 14 ODBASE 08 - Monterrey, México Thursday, August 8, 13
  • 45. al supervisedBy @1!! al is in the domain of supervisedBy every PhD is also in the domain of supervisedBy ➔ 2 al is a PhD ➔ 5 1.PhD ⊑ Student 2.PhD ⊑ dom(supervisedBy) 3.range(supervisedBy) ⊑ Professor 4.Professor ⊑ domain(teaches) 5.PhD(al) @1 teaches @2 al is a Student every PhD is also a Student ➔ 1 al is a PhD ➔ 5 q(x) :- Student(x), supervisedBy(x,y), teaches(y,z) q(al) 14 ODBASE 08 - Monterrey, México Thursday, August 8, 13
  • 46. al supervisedBy @1!! al is in the domain of supervisedBy every PhD is also in the domain of supervisedBy ➔ 2 al is a PhD ➔ 5 1.PhD ⊑ Student 2.PhD ⊑ dom(supervisedBy) 3.range(supervisedBy) ⊑ Professor 4.Professor ⊑ domain(teaches) 5.PhD(al) @1 teaches @2 every Professor is in the domain of teaches ➔ 4 @1 is a Professor al is a Student every PhD is also a Student ➔ 1 al is a PhD ➔ 5 q(x) :- Student(x), supervisedBy(x,y), teaches(y,z) q(al) 14 ODBASE 08 - Monterrey, México Thursday, August 8, 13
  • 47. al supervisedBy @1!! al is in the domain of supervisedBy every PhD is also in the domain of supervisedBy ➔ 2 al is a PhD ➔ 5 1.PhD ⊑ Student 2.PhD ⊑ dom(supervisedBy) 3.range(supervisedBy) ⊑ Professor 4.Professor ⊑ domain(teaches) 5.PhD(al) @1 teaches @2 every Professor is in the domain of teaches ➔ 4 @1 is a Professor @1 is in the range of supervisedBy everything in the range of supervisedBy is a Professor ➔ 3 al is a Student every PhD is also a Student ➔ 1 al is a PhD ➔ 5 q(x) :- Student(x), supervisedBy(x,y), teaches(y,z) q(al) 14 ODBASE 08 - Monterrey, México Thursday, August 8, 13
  • 48. al supervisedBy @1!! al is in the domain of supervisedBy every PhD is also in the domain of supervisedBy ➔ 2 al is a PhD ➔ 5 1.PhD ⊑ Student 2.PhD ⊑ dom(supervisedBy) 3.range(supervisedBy) ⊑ Professor 4.Professor ⊑ domain(teaches) 5.PhD(al) @1 teaches @2 every Professor is in the domain of teaches ➔ 4 @1 is a Professor @1 is in the range of supervisedBy everything in the range of supervisedBy is a Professor ➔ 3 al is a Student every PhD is also a Student ➔ 1 al is a PhD ➔ 5 q(x) :- Student(x), supervisedBy(x,y), teaches(y,z) q(al) 14 ODBASE 08 - Monterrey, México Thursday, August 8, 13
  • 49. Query Answering 15 ODBASE 08 - Monterrey, México Thursday, August 8, 13
  • 50. Query Answering • Successful queries • Failed queries 15 ODBASE 08 - Monterrey, México Thursday, August 8, 13
  • 51. Query Answering • Successful queries • Failed queries • Due to missing information 15 ODBASE 08 - Monterrey, México Thursday, August 8, 13
  • 52. Query Answering • Successful queries • Failed queries • Due to missing information • Due to unsatisfiability 15 ODBASE 08 - Monterrey, México Thursday, August 8, 13
  • 53. 1.PhD ⊑ Student 2.range(supervisedBy) ⊑ Professor 3.disjoint(Professor,Student) q(x):- PhD(x), supervisedBy(y, x) q inconsistent 16 ODBASE 08 - Monterrey, México Thursday, August 8, 13
  • 54. @1 is a Professor @1 is a Student 1.PhD ⊑ Student 2.range(supervisedBy) ⊑ Professor 3.disjoint(Professor,Student) q(x):- PhD(x), supervisedBy(y, x) q inconsistent q(@1) :- PhD(@1), supervisedBy(@2, @1) no Student can be a Professor ➔ 3 16 ODBASE 08 - Monterrey, México Thursday, August 8, 13
  • 55. @1 is a Professor @1 is a Student @1 is a PhD every PhD is also a Student ➔ 1 1.PhD ⊑ Student 2.range(supervisedBy) ⊑ Professor 3.disjoint(Professor,Student) q(x):- PhD(x), supervisedBy(y, x) q inconsistent q(@1) :- PhD(@1), supervisedBy(@2, @1) no Student can be a Professor ➔ 3 16 ODBASE 08 - Monterrey, México Thursday, August 8, 13
  • 56. @1 is a Professor @1 is a Student @1 is a PhD every PhD is also a Student ➔ 1 1.PhD ⊑ Student 2.range(supervisedBy) ⊑ Professor 3.disjoint(Professor,Student) q(x):- PhD(x), supervisedBy(y, x) q inconsistent q(@1) :- PhD(@1), supervisedBy(@2, @1) no Student can be a Professor ➔ 3 16 ODBASE 08 - Monterrey, México Thursday, August 8, 13
  • 57. @1 is a Professor @2 supervisedBy @1 everything in the range of supervisedBy is a Professor ➔ 2 @1 is a Student @1 is a PhD every PhD is also a Student ➔ 1 1.PhD ⊑ Student 2.range(supervisedBy) ⊑ Professor 3.disjoint(Professor,Student) q(x):- PhD(x), supervisedBy(y, x) q inconsistent q(@1) :- PhD(@1), supervisedBy(@2, @1) no Student can be a Professor ➔ 3 16 ODBASE 08 - Monterrey, México Thursday, August 8, 13
  • 58. @1 is a Professor @2 supervisedBy @1 everything in the range of supervisedBy is a Professor ➔ 2 @1 is a Student @1 is a PhD every PhD is also a Student ➔ 1 1.PhD ⊑ Student 2.range(supervisedBy) ⊑ Professor 3.disjoint(Professor,Student) q(x):- PhD(x), supervisedBy(y, x) q inconsistent q(@1) :- PhD(@1), supervisedBy(@2, @1) no Student can be a Professor ➔ 3 16 ODBASE 08 - Monterrey, México Thursday, August 8, 13
  • 59. Conclusions • We addressed DL-Lite explanations can be given for traditional reasoning services (shortness of proof). • Finite model case. • Explaining conjunctive queries when reasoning is present by a) exploiting existing DL-Lite query rewriting algorithm and b) Prolog-based program. • We looked into the problem of explaining failed answers to queries. 17 ODBASE 08 - Monterrey, México Thursday, August 8, 13
  • 60. Future Work • Explanation of failed answers to CQs over DL-Lite ontologies. • Integration of prototype with the QuOnto Reasoner. • Explanation in Ontology Based Data Access (presence of mappings). • Field testing of the algorithms. 18 ODBASE 08 - Monterrey, México Thursday, August 8, 13