This document presents a master's thesis on SPARQL query rewriting with paths, focusing on eliminating projection in queries to address the NP-completeness of query evaluation. It details methods for rewriting queries by eliminating non-distinguished variables, providing examples and transformation rules for various query structures. The document also discusses related works and outlines a systematic approach to optimizing SPARQL queries through path patterns.

1. SPARQL Query Rewriting with Paths
SPARQL Query Rewriting with Paths
Master of Science in Informatics at Grenoble
Artificial Intelligence and the Web (AIW)
Abdullah Abbas
23 June 2014
Supervisors:
Dr. Jérôme Euzenat, INRIA
Dr. Nabil Layaida, INRIA
Dr. Pierre Genevès, CNRS
Jury:
Prof. Catherine Berrut
Prof. Éric Gaussier
Dr. Noha Ibrahim
Dr. Jean-Marc Vincent
Dr. Pierre Genevès
Dr. Jean-Yves Vion-Dury
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2. SPARQL Query Rewriting with Paths
RDF (Resource Description Framework)
RDF Triple
<subject> <predicate> <object>
{
exp:p1 exp:named "Alice".
exp:p1 exp:likes exp:tennis.
exp:p2 exp:named "Bob".
exp:p2 exp:likes exp:soccer.
}
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3. SPARQL Query Rewriting with Paths
RDF (Resource Description Framework)
RDF Triple
<subject> <predicate> <object>
{
exp:p1 exp:named "Alice".
exp:p1 exp:likes exp:tennis.
exp:p2 exp:named "Bob".
exp:p2 exp:likes exp:soccer.
}
p1
“Alice”named
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4. SPARQL Query Rewriting with Paths
RDF (Resource Description Framework)
RDF Graph
<subject> <predicate> <object>
{
exp:p1 exp:named "Alice".
exp:p1 exp:likes exp:tennis.
exp:p2 exp:named "Bob".
exp:p2 exp:likes exp:soccer.
}
p1
“Alice”
tennis
p2
“Bob”
soccer
named
likes
named
likes
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5. SPARQL Query Rewriting with Paths
SPARQL Query
RDF Graph
{
exp:p1 exp:named "Alice".
exp:p1 exp:likes exp:tennis.
exp:p2 exp:named "Bob".
exp:p2 exp:likes exp:soccer.
}
p1
“Alice”
tennis
p2
“Bob”
soccer
named
likes
named
likes
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6. SPARQL Query Rewriting with Paths
SPARQL Query
RDF Graph
{
exp:p1 exp:named "Alice".
exp:p1 exp:likes exp:tennis.
exp:p2 exp:named "Bob".
exp:p2 exp:likes exp:soccer.
}
SPARQL query
SELECT ?name
WHERE
{
?p exp:likes exp:tennis.
?p exp:named ?name.
}
p1
“Alice”
tennis
p2
“Bob”
soccer
named
likes
named
likes
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7. SPARQL Query Rewriting with Paths
SPARQL Query
RDF Graph
{
exp:p1 exp:named "Alice".
exp:p1 exp:likes exp:tennis.
exp:p2 exp:named "Bob".
exp:p2 exp:likes exp:soccer.
}
SPARQL query
SELECT ?name
WHERE
{
?p exp:likes exp:tennis.
?p exp:named ?name.
}
p1
“Alice”
tennis
p2
“Bob”
soccer
?p
?name
tennis
named
likes
named
likes
named
likes
3/30

8. SPARQL Query Rewriting with Paths
SPARQL Query
RDF Graph
{
exp:p1 exp:named "Alice".
exp:p1 exp:likes exp:tennis.
exp:p2 exp:named "Bob".
exp:p2 exp:likes exp:soccer.
}
SPARQL query
SELECT ?name
WHERE
{
?p exp:likes exp:tennis.
?p exp:named ?name.
}
p1
“Alice”
tennis
p2
“Bob”
soccer
?p
?name
tennis
named
likes
named
likes
named
likes
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9. SPARQL Query Rewriting with Paths
Motivation:
Query evaluation with projection is NP-Complete.
4/30

10. SPARQL Query Rewriting with Paths
Motivation:
Query evaluation with projection is NP-Complete.
Purpose:
Our purpose is to suppress projection in SPARQL queries.
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11. SPARQL Query Rewriting with Paths
Motivation:
Query evaluation with projection is NP-Complete.
Purpose:
Our purpose is to suppress projection in SPARQL queries.
How?
We rewrite SPARQL queries in order to eliminate
non-distinguished variables.
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12. SPARQL Query Rewriting with Paths
Example
Consider the following SPARQL query:
SELECT ?book
WHERE
{
?book rdf:type exp:book.
?book exp:author ?author.
?author exp:name "Donald Knuth".
?library exp:sells ?book.
?library exp:in ?city.
?city exp:name "Paris"
}
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13. SPARQL Query Rewriting with Paths
Example
Transformation!
SELECT ?book
WHERE
{
?book rdf:type exp:book.
?book exp:author/exp:name "Donald Knuth".
?book ^exp:sells/exp:in/exp:name "Paris"
}
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14. SPARQL Query Rewriting with Paths
Example
Consider the following SPARQL query:
SELECT ?book
WHERE
{
?book rdf:type exp:book.
?book exp:author ?author.
?author exp:name "Donald Knuth".
?library exp:sells ?book.
?library exp:in ?city.
?city exp:name "Paris"
}
Transformation!
SELECT ?book
WHERE
{
?book rdf:type exp:book.
?book exp:author/exp:name "Donald Knuth".
?book ^exp:sells/exp:in/exp:name "Paris"
}
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15. SPARQL Query Rewriting with Paths
Table of Contents
1 SPARQL
Fragments
Paths
2 Related Works
3 SPARQL Query Rewriting
4 Constrained Datasets
5 Dealing with Non-Constrained Datasets
6 Conclusion
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16. SPARQL Query Rewriting with Paths
SPARQL
Table of Contents
1 SPARQL
Fragments
Paths
2 Related Works
3 SPARQL Query Rewriting
4 Constrained Datasets
5 Dealing with Non-Constrained Datasets
6 Conclusion
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17. SPARQL Query Rewriting with Paths
SPARQL
Fragments
SPARQL Query (AND, UNION, OPTIONAL, FILTER)
Basic SPARQL Query
SELECT ?name
WHERE
{
?p exp:likes exp:tennis.
?p exp:named ?name.
}
.
{
?p exp:likes exp:tennis.
?p exp:named ?name.
}
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18. SPARQL Query Rewriting with Paths
SPARQL
Fragments
SPARQL Query (AND, UNION, OPTIONAL, FILTER)
AND
SELECT ?name
WHERE
{
?p exp:likes exp:tennis.
?p exp:named ?name.
}
.
{
?p exp:likes exp:tennis.
?p exp:named ?name.
}
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19. SPARQL Query Rewriting with Paths
SPARQL
Fragments
SPARQL Query (AND, UNION, OPTIONAL, FILTER)
AND
SELECT ?name
WHERE
{
?p exp:likes exp:tennis.
?p exp:named ?name.
}
.
{
?p exp:likes exp:tennis.
?p exp:named ?name.
}
UNION
SELECT ?name
WHERE
{
?p exp:likes exp:tennis.
?p exp:named ?name.
}
UNION
{
?p exp:likes exp:tennis.
?p exp:named ?name.
}
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20. SPARQL Query Rewriting with Paths
SPARQL
Fragments
SPARQL Query (AND, UNION, OPTIONAL, FILTER)
OPTIONAL
SELECT ?name, ?other
WHERE
{
?p exp:likes exp:tennis.
?p exp:named ?name.
}
OPTIONAL
{
?p exp:likes ?other.
?p exp:named ?name.
}
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21. SPARQL Query Rewriting with Paths
SPARQL
Fragments
SPARQL Query (AND, UNION, OPTIONAL, FILTER)
OPTIONAL
SELECT ?name, ?other
WHERE
{
?p exp:likes exp:tennis.
?p exp:named ?name.
}
OPTIONAL
{
?p exp:likes ?other.
?p exp:named ?name.
}
FILTER
SELECT ?name
WHERE
{
?p exp:likes exp:tennis.
?p exp:named ?name.
?p exp:age ?age
FILTER (?age<20)
}
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22. SPARQL Query Rewriting with Paths
SPARQL
Paths
Paths
In SPARQL 1.1 called property path patterns.
Syntax Form Example
ˆp Bob ^likes Alice ≡ Alice likes Bob
p1/p2 Alice likes/in Paris
p1|p2 Grenoble train|bus Lyon
p∗ Alice parent* Bob
p+ Alice parent+ Bob
p? Alice likes? ?x
!p
(p)
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23. SPARQL Query Rewriting with Paths
Related Works
Table of Contents
1 SPARQL
Fragments
Paths
2 Related Works
3 SPARQL Query Rewriting
4 Constrained Datasets
5 Dealing with Non-Constrained Datasets
6 Conclusion
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24. SPARQL Query Rewriting with Paths
Related Works
Related Works I
SPARQL Query Optimization
Michael Schmidt, Michael Meier, Georg Lausen. Foundations of
SPARQL Query Optimization. In Proc. ICDT, New York (NY, US), pp4-33,
2010
Jorge Pérez, Marcelo Arenas, Claudio Gutierrez. Semantics and
complexity of SPARQL. ACM Transactions on Database Systems
34(3):16, 2009
F. Alkhateeb, J. Baget and J. Euzenat, ’Constrained Regular
Expressions in SPARQL’, iss -6360, p. 23, 2007.
Description Logics
D. Calvanese, G. De Giacomo, D. Lembo, M. Lenzerini and R. Rosati,
’Tractable Reasoning and Efficient Query Answering in Description
Logics: The DL-Lite Family’, Journal of Automated Reasoning, vol 39,
iss 3, pp. 385-429, 2007.
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25. SPARQL Query Rewriting with Paths
Related Works
Related Works II
SPARQL Static Analysis
Melisachew Wudage Chekol. Static analysis of semantic web queries.
Thése d’informatique, Université de Grenoble, 2012
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26. SPARQL Query Rewriting with Paths
SPARQL Query Rewriting
Table of Contents
1 SPARQL
Fragments
Paths
2 Related Works
3 SPARQL Query Rewriting
4 Constrained Datasets
5 Dealing with Non-Constrained Datasets
6 Conclusion
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27. SPARQL Query Rewriting with Paths
SPARQL Query Rewriting
SPARQL Query Rewriting
PART 1: We define a variable eliminating function:
ζx(P) =
1 We get the property path patterns that contain ?x.
2 We adjust the position of ?x to become in subject position
for all of the property path patterns.
3 We define a supplementary transformation function (ζ ).
4 AND, UNION, OPTIONAL, and FILTER are conserved in
position.
PART 2: We apply the variable eliminating function to
each non-distinguished variable in a query.
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28. SPARQL Query Rewriting with Paths
SPARQL Query Rewriting
SPARQL Query Rewriting
PART 1: We define a variable eliminating function:
ζx(P) =
1 We get the property path patterns that contain ?x.
2 We adjust the position of ?x to become in subject position
for all of the property path patterns.
3 We define a supplementary transformation function (ζ ).
4 AND, UNION, OPTIONAL, and FILTER are conserved in
position.
PART 2: We apply the variable eliminating function to
each non-distinguished variable in a query.
18/30

29. SPARQL Query Rewriting with Paths
SPARQL Query Rewriting
SPARQL Query Rewriting
PART 1: We define a variable eliminating function:
ζx(P) =
1 We get the property path patterns that contain ?x.
2 We adjust the position of ?x to become in subject position
for all of the property path patterns.
3 We define a supplementary transformation function (ζ ).
4 AND, UNION, OPTIONAL, and FILTER are conserved in
position.
PART 2: We apply the variable eliminating function to
each non-distinguished variable in a query.
18/30

30. SPARQL Query Rewriting with Paths
SPARQL Query Rewriting
SPARQL Query Rewriting
PART 1: We define a variable eliminating function:
ζx(P) = αx(P) ∪ ¯αx(P)
1 We get the property path patterns that contain ?x.
2 We adjust the position of ?x to become in subject position
for all of the property path patterns.
3 We define a supplementary transformation function (ζ ).
4 AND, UNION, OPTIONAL, and FILTER are conserved in
position.
PART 2: We apply the variable eliminating function to
each non-distinguished variable in a query.
18/30

31. SPARQL Query Rewriting with Paths
SPARQL Query Rewriting
SPARQL Query Rewriting
PART 1: We define a variable eliminating function:
ζx(P) = δx(αx(P)) ∪ ¯αx(P)
1 We get the property path patterns that contain ?x.
2 We adjust the position of ?x to become in subject position
for all of the property path patterns.
3 We define a supplementary transformation function (ζ ).
4 AND, UNION, OPTIONAL, and FILTER are conserved in
position.
PART 2: We apply the variable eliminating function to
each non-distinguished variable in a query.
18/30

32. SPARQL Query Rewriting with Paths
SPARQL Query Rewriting
SPARQL Query Rewriting
PART 1: We define a variable eliminating function:
ζx(P) = ζ (δx(αx(P))) ∪ ¯αx(P)
1 We get the property path patterns that contain ?x.
2 We adjust the position of ?x to become in subject position
for all of the property path patterns.
3 We define a supplementary transformation function (ζ ).
4 AND, UNION, OPTIONAL, and FILTER are conserved in
position.
PART 2: We apply the variable eliminating function to
each non-distinguished variable in a query.
18/30

33. SPARQL Query Rewriting with Paths
SPARQL Query Rewriting
SPARQL Query Rewriting
PART 1: We define a variable eliminating function:
ζx(P) = ζ (δx(αx(P))) ∪ ¯αx(P)
1 We get the property path patterns that contain ?x.
2 We adjust the position of ?x to become in subject position
for all of the property path patterns.
3 We define a supplementary transformation function (ζ ).
4 AND, UNION, OPTIONAL, and FILTER are conserved in
position.
PART 2: We apply the variable eliminating function to
each non-distinguished variable in a query.
18/30

34. SPARQL Query Rewriting with Paths
SPARQL Query Rewriting
SPARQL Query Rewriting
PART 1: We define a variable eliminating function:
ζx(P) = ζ (δx(αx(P))) ∪ ¯αx(P)
1 We get the property path patterns that contain ?x.
2 We adjust the position of ?x to become in subject position
for all of the property path patterns.
3 We define a supplementary transformation function (ζ ).
4 AND, UNION, OPTIONAL, and FILTER are conserved in
position.
PART 2: We apply the variable eliminating function to
each non-distinguished variable in a query.
Transformation complexity: O(n3
)
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35. SPARQL Query Rewriting with Paths
SPARQL Query Rewriting
Non-Eliminatable Variables
CASE 1: Free variable
Variable that only has relations with itself.
CASE 2: Variable appearing in a FILTER
If eliminated, the filter constraint will loose reference to
the variable.
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36. SPARQL Query Rewriting with Paths
SPARQL Query Rewriting
Non-Eliminatable Variables
CASE 1: Free variable
Variable that only has relations with itself.
CASE 2: Variable appearing in a FILTER
If eliminated, the filter constraint will loose reference to
the variable.
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37. SPARQL Query Rewriting with Paths
SPARQL Query Rewriting
Suplementary tranformation rule 1
Single Property Path Pattern
?x P1 O1
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38. SPARQL Query Rewriting with Paths
SPARQL Query Rewriting
Suplementary tranformation rule 1
Single Property Path Pattern
?x P1 O1
can be rewritten as:
O1 ^P1/P1 O1
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39. SPARQL Query Rewriting with Paths
SPARQL Query Rewriting
Suplementary tranformation rule 2
Double Property Path Patterns
{
?x P1 O1.
?x P2 O2
}
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40. SPARQL Query Rewriting with Paths
SPARQL Query Rewriting
Suplementary tranformation rule 2
Double Property Path Patterns
{
?x P1 O1.
?x P2 O2
}
can be rewritten as:
{
O1 ^P1/P2 O2
}
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41. SPARQL Query Rewriting with Paths
SPARQL Query Rewriting
Suplementary tranformation rule 3
Multiple Property Path Patterns
{
?x P1 O1.
?x P2 O2.
?x P3 O3
}
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42. SPARQL Query Rewriting with Paths
SPARQL Query Rewriting
Suplementary tranformation rule 3
Multiple Property Path Patterns
{
?x P1 O1.
?x P2 O2.
?x P3 O3
}
x
O1 O2
O3
P1 P2
P3
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43. SPARQL Query Rewriting with Paths
SPARQL Query Rewriting
Suplementary tranformation rule 3
Multiple Property Path Patterns
{
?x P1 O1.
?x P2 O2.
?x P3 O3
}
x
O1 O2
O3
P1 P2
P3
Suggestion:
Using property path expression
O1 ^P1/P2/^P2/P3 O3
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44. SPARQL Query Rewriting with Paths
SPARQL Query Rewriting
Suplementary tranformation rule 3
Multiple Property Path Patterns
{
?x P1 O1.
?x P2 O2.
?x P3 O3
}
x
O1 O2
O3
P1 P2
P3
Suggestion:
Using property path expression
O1 ^P1/P2/^P2/P3 O3
xO1
O2
O3
P1
P2
P3
y
P2
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45. SPARQL Query Rewriting with Paths
SPARQL Query Rewriting
Suplementary tranformation rule 3
Multiple Property Path Patterns
{
?x P1 O1.
?x P2 O2.
?x P3 O3
}
x
O1 O2
O3
P1 P2
P3
Path 1
Path 2
Suggestion:
Using conjunction of paths
{
O1 ^P1/P2 O1.
O1 ^P1/P3 O3
}
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46. SPARQL Query Rewriting with Paths
SPARQL Query Rewriting
Suplementary tranformation rule 3
Multiple Property Path Patterns
{
?x P1 O1.
?x P2 O2.
?x P3 O3
}
x
O1 O2
O3
P1 P2
P3
Path 1
Path 2
Suggestion:
Using conjunction of paths
{
O1 ^P1/P2 O1.
O1 ^P1/P3 O3
}
x
O1
O2
O3
P1
P2
P3
yP1
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47. SPARQL Query Rewriting with Paths
SPARQL Query Rewriting
Suplementary tranformation rule 3
Multiple Property Path Patterns
{
?x P1 O1.
?x P2 O2.
?x P3 O3
}
x
O1 O2
O3
P1 P2
P3
Path 1
Path 3 Path 2
Adopted transformation:
Cycle
{
O1 ^P1/P2 O1.
O2 ^P2/P3 O3.
O3 ^P3/P1 O1
}
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48. SPARQL Query Rewriting with Paths
SPARQL Query Rewriting
Suplementary tranformation rule 3
Multiple Property Path Patterns
{
?x P1 O1.
?x P2 O2.
?x P3 O3
}
x
O1 O2
O3
P1 P2
P3
Path 1
Path 3 Path 2
Adopted transformation:
Cycle
{
O1 ^P1/P2 O1.
O2 ^P2/P3 O3.
O3 ^P3/P1 O1
}
x
O1 O2
O3
P1
P2
P3
yz
P1
P2
P3
x
O1 O2
O3
P1
P2
P3
y
P1
P2
x
O1 O2
O3
P1
P2
P3
y
P1
P3
x
O1 O2
O3
P2
P3
y
P1
P2
P3
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49. SPARQL Query Rewriting with Paths
Constrained Datasets
Table of Contents
1 SPARQL
Fragments
Paths
2 Related Works
3 SPARQL Query Rewriting
4 Constrained Datasets
5 Dealing with Non-Constrained Datasets
6 Conclusion
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50. SPARQL Query Rewriting with Paths
Constrained Datasets
Constrained Datasets
Desired result:
x
O1 O2
O3
P1 P2
P3
Path 1
Path 3 Path 2
Undesired results:
x
O1 O2
O3
P1
P2
P3
yz
P1
P2
P3
x
O1 O2
O3
P1
P2
P3
y
P1
P2
x
O1 O2
O3
P1
P2
P3
y
P1
P3
x
O1 O2
O3
P2
P3
y
P1
P2
P3
Constraint 1: Acyclic graph
Constraint 2: Functionality of properties
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51. SPARQL Query Rewriting with Paths
Constrained Datasets
Constrained Datasets
Desired result:
x
O1 O2
O3
P1 P2
P3
Path 1
Path 3 Path 2
Undesired results:
x
O1 O2
O3
P1
P2
P3
yz
P1
P2
P3
x
O1 O2
O3
P1
P2
P3
y
P1
P2
x
O1 O2
O3
P1
P2
P3
y
P1
P3
x
O1 O2
O3
P2
P3
y
P1
P2
P3
Constraint 1: Acyclic graph
Constraint 2: Functionality of properties
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52. SPARQL Query Rewriting with Paths
Constrained Datasets
Constrained Datasets
Desired result:
x
O1 O2
O3
P1 P2
P3
Path 1
Path 3 Path 2
Undesired results:
x
O1 O2
O3
P1
P2
P3
yz
P1
P2
P3
x
O1 O2
O3
P1
P2
P3
y
P1
P2
x
O1 O2
O3
P1
P2
P3
y
P1
P3
x
O1 O2
O3
P2
P3
y
P1
P2
P3
Constraint 1: Acyclic graph
Constraint 2: Functionality of properties
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53. SPARQL Query Rewriting with Paths
Dealing with Non-Constrained Datasets
Table of Contents
1 SPARQL
Fragments
Paths
2 Related Works
3 SPARQL Query Rewriting
4 Constrained Datasets
5 Dealing with Non-Constrained Datasets
6 Conclusion
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54. SPARQL Query Rewriting with Paths
Dealing with Non-Constrained Datasets
Dealing with Non-Constrained Datasets
𝜁 𝑥(𝜁 𝑦(𝑃))
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55. SPARQL Query Rewriting with Paths
Dealing with Non-Constrained Datasets
Dealing with Non-Constrained Datasets
𝜁 𝑥(𝜁 𝑦(𝑃))
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56. SPARQL Query Rewriting with Paths
Conclusion
Table of Contents
1 SPARQL
Fragments
Paths
2 Related Works
3 SPARQL Query Rewriting
4 Constrained Datasets
5 Dealing with Non-Constrained Datasets
6 Conclusion
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57. SPARQL Query Rewriting with Paths
Conclusion
Conclusion
Motivation: Non-distinguished variable elimination
reduces the computation complexity.
Finding: Non-distinguished variable elimination is not
always possible.
Contribution 1: We proposed a query transformation
function that eliminates non-distinguished variables
where such elimination is possible.
Our transformation function is always complete but not
always sound.
Contribution 2: We defined constraints that makes our
transformation complete and sound.
Contribution 3: We proposed an alternative solution for
dealing with non-constrained datasets.
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58. SPARQL Query Rewriting with Paths
Conclusion
Conclusion
Motivation: Non-distinguished variable elimination
reduces the computation complexity.
Finding: Non-distinguished variable elimination is not
always possible.
Contribution 1: We proposed a query transformation
function that eliminates non-distinguished variables
where such elimination is possible.
Our transformation function is always complete but not
always sound.
Contribution 2: We defined constraints that makes our
transformation complete and sound.
Contribution 3: We proposed an alternative solution for
dealing with non-constrained datasets.
28/30

59. SPARQL Query Rewriting with Paths
Conclusion
Conclusion
Motivation: Non-distinguished variable elimination
reduces the computation complexity.
Finding: Non-distinguished variable elimination is not
always possible.
Contribution 1: We proposed a query transformation
function that eliminates non-distinguished variables
where such elimination is possible.
Our transformation function is always complete but not
always sound.
Contribution 2: We defined constraints that makes our
transformation complete and sound.
Contribution 3: We proposed an alternative solution for
dealing with non-constrained datasets.
28/30

60. SPARQL Query Rewriting with Paths
Conclusion
Conclusion
Motivation: Non-distinguished variable elimination
reduces the computation complexity.
Finding: Non-distinguished variable elimination is not
always possible.
Contribution 1: We proposed a query transformation
function that eliminates non-distinguished variables
where such elimination is possible.
Our transformation function is always complete but not
always sound.
Contribution 2: We defined constraints that makes our
transformation complete and sound.
Contribution 3: We proposed an alternative solution for
dealing with non-constrained datasets.
28/30

61. SPARQL Query Rewriting with Paths
Conclusion
Conclusion
Motivation: Non-distinguished variable elimination
reduces the computation complexity.
Finding: Non-distinguished variable elimination is not
always possible.
Contribution 1: We proposed a query transformation
function that eliminates non-distinguished variables
where such elimination is possible.
Our transformation function is always complete but not
always sound.
Contribution 2: We defined constraints that makes our
transformation complete and sound.
Contribution 3: We proposed an alternative solution for
dealing with non-constrained datasets.
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62. SPARQL Query Rewriting with Paths
Conclusion
Conclusion
Motivation: Non-distinguished variable elimination
reduces the computation complexity.
Finding: Non-distinguished variable elimination is not
always possible.
Contribution 1: We proposed a query transformation
function that eliminates non-distinguished variables
where such elimination is possible.
Our transformation function is always complete but not
always sound.
Contribution 2: We defined constraints that makes our
transformation complete and sound.
Contribution 3: We proposed an alternative solution for
dealing with non-constrained datasets.
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63. SPARQL Query Rewriting with Paths
Conclusion
Further Perspectives
Static analysis of SPARQL queries (containment,
equivalence):
The completeness of our transformation function in
general, and its soundness and completeness in specific
environments are basic ideas, over which we can build
further SPARQL query analysis.
The impact of ontologies on our transformation and how
to take advantage of them
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64. SPARQL Query Rewriting with Paths
Conclusion
Further Perspectives
Static analysis of SPARQL queries (containment,
equivalence):
The completeness of our transformation function in
general, and its soundness and completeness in specific
environments are basic ideas, over which we can build
further SPARQL query analysis.
The impact of ontologies on our transformation and how
to take advantage of them
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65. SPARQL Query Rewriting with Paths
Conclusion
Thank You
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