Optimized index structures for querying rdf from the web

1. Optimized Index Structures
for Querying RDF from the
Web
Presented by : Mahdi Atawna
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2. About the Paper
 The paper was published at the Third Latin
American Web Congress in 2005
 Have 56 citation.
Andreas Harth
National University
of Galway, Ireland
Prof. Stefan Decker
National University of
Galway, Ireland
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3. Outline
 Overview of Semantic Web.
 Overview of Indexes.
 Paper motivation.
 Methodology.
 Experiment & Result.
 Conclusion.
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4. Semantic Web
 Also called :
 Web 3.0.
 the Linked Data Web.
 the Web of Data…whatever you call it.
 the next major evolution in connecting information.
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5. Why semantic web?
 It enables data to be linked from a source to any
other source.
 It can be understood by computers so that they can
perform increasingly sophisticated tasks on our
behalf.
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6. 6
Source: http://lod-cloud.net

7. Semantic Web Standards
 RDF (Resource Description Framework): The data modeling
language for the Semantic Web (like UML). All Semantic Web
information is stored and represented in the RDF.
 SPARQL : The query language of the Semantic Web.
 OWL (Web Ontology Language) The schema language, or
knowledge representation (KR) language, of the Semantic Web.
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8. What is RDF?
 RDF is the data model of the Semantic Web.
 That means that all data in Semantic Web
technologies is represented as RDF.
 If you store Semantic Web data, it's in RDF.
 If you query Semantic Web data (typically using
SPARQL), it's RDF data. If you send Semantic Web
data to your friend, it's RDF.
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9. 9

10. 10
Source : http://www.w3.org/TR/rdf11-primer/

11. RDF triples
 are representations of graph edges.
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Subject Object
Predicate
Mahdi Hebron
born in

12. RDF example
@prefix foaf: <http://xmlns.com/foaf/0.1/ .
<http://example.org/bob#me>
foaf:topic_interest
<http://wikidata.org/entity/Q12418> .
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13. RDF example
@prefix foaf: <http://xmlns.com/foaf/0.1/ .
<http://example.org/bob#me>
foaf:topic_interest
<http://wikidata.org/entity/Q12418> .
Subject
predicate
Object
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14. Sparql query language
SELECT ?p ?o
{
<http://nasa.dataincubator.org/spacecraft/1968-089A> ?p ?o
}
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15. What is database index?
 A database index is a data structure that improves the
speed of data retrieval operations on a database table at
the cost of additional writes and storage space to maintain
the index data structure.
 Index goal : The index structure enables fast retrieval of
data
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16. 16

17. Index example
key articles
Leonardo [104,70,12,98]
Mona Lisa [2,201,7,20,12]
Francesco [1,8,900,104]
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18. Paper motivation
 Previous Systems provide a storage infrastructure for RDF data, but
index structure which do not support typical query scenarios
for data from the Web which results in poor query answering
performance in some cases.
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19. Methodology
 The researchers present a new index structure that handle the
data from the Web .
 Implemented the index structure in a lightweight software called
YARS
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20. RDF Index structures
 The authors suggested an index structure that contains two
sets:
1. Lexicon : covers the string representation of RDF graph
(r,l,b)
2. Quad indexes : cover the quads (triples).
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21. 1. Lexicon indexes
 NodeOID and OIDNode Index
 Keyword Index
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22. 1. Lexicon indexes
 NodeOID and OIDNode Index :
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Key value
<http://www.harth.org/andreas/#me> 3
<http://decker.cn/stefan/> 14
<http://sw.deri.org/ aharth/foaf.rdf> 11
<http://www.deri.org/> 1

23. 1. Lexicon indexes
 Keyword Index (Popular in search engines)
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Key No of hits List of hits
“Andreas” 1 3
“Decker” 1 11
”Harth” 1 3
“Stefan” 2 11,13

24. 2. Quad indexes
 Access Patterns
 Combined Indexes
 Occurrence Counts
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25. 2. Quad indexes
A- Access Patterns
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No Access pattern No Access pattern
1 (?:?:?:?) 9 (s:?:o:c)
2 (s:?:?:?) 10 (?:?:o:c)
3 (s:p:?:?) 11 (?:?:o:?)
4 (s:p:o:?) 12 (?:?:?:c)
5 (s:p:o:c) 13 (s:?:?:c)
6 (?:p:?:?) 14 (s:p:?:c)
7 (?:p:o:?) 15 (?:p:?:c)
8 (?:p:o:c) 16 (s:?:o:?)

26. 2. Quad indexes
A- Access Patterns
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No Access pattern values
1 (?:?:?:?) [1,5,3]
2 (s:?:?:?) [2]
3 (s:p:?:?) [9,8,2,3]
4 (s:p:o:?) [1,3]
5 (s:p:o:c) [1]
6 (?:p:?:?) [76,9]
7 (?:p:o:?) [2,3]

27. 2. Quad indexes
B- Combined Indexes
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spoc poc osc csp cp os
(?:?:?:?) (?:p:?:?) (?:?:o:?)
(?:?:?:c) (?:p:?:c) (s:?:o:?)
(s:?:?:?) (?:p:o:?) (?:?:o:c)
(s:?:?:c)
(s:p:?:?) (?:p:o:c) (s:?:o:c)
(s:p:?:c)
(s:p:o:?)
(s:p:o:c)

28. 2. Quad indexes
C- Occurrence Counts
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No
Access
pattern
values count
1 (?:?:?:?) [1,5,3] 3
2 (s:?:?:?) [2] 1
3 (s:p:?:?) [9,8,2,3] 4
4 (s:p:o:?) [1,3] 2
5 (s:p:o:c) [1] 1
6 (?:p:?:?) [76,9] 2
7 (?:p:o:?) [2,3] 2

29. YARS
 Web application that built in JAVA.
 Has two parts:
 a storage component that handles both persistent and in-
memory indexes.
 a query handler to perform query processing and evaluation.
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30. Experiment
 They evaluated the performance based on a dataset of 2.8
million triples (293 MB).
 The testing server has :
 Pentium-4 2.4 GHz
 4 GB RAM
 running Debian Sarge .
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31. Experiment
They considered the following RDF stores for evaluation:
 Sesame.
 Kowari (failed to get a running version).
 Redland.
 Jena2. ([9] shows that Sesame generally supersedes Jena in
performance results)
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32. Experiment
Experiment parts:
 Index Construction.
 Queries.
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33. Result – index construction
System Index size (bytes)
Redland 2.164.019.200
Sesame MySQL 340.381.636
Sesame native 39.997.992
YARS 1.090.002.944
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Table 8: Index size for the synthetic Univ20 dataset.

34. Result – index construction
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35. Result - queries
No Query
1 ?x rdf:type univ:UndergraduateStudent
2 ?x ?p ”UndergraduateStudent0”
3 <http://www.University965.edu> ?p ?o
4 ?x univ:worksFor ?y
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Query Redland Sesame MySQL Sesame Native YARS
1 0:10.48 0:18.87 1:05.16 0:18.41
2 0:44.14 0:00.73 0:00.55 0:00.49
3 0:44.15 0:00.46 0:00.47 0:00.32
4 3:04.21 0:03.42 0:01.95 0:00.47
Performance results for quad queries.

36. Conclusion
 The auther introduced query processing for RDF which is an I
portant issue in sematic web.
 YARS has some overhead for resolving the dependencies and
order in comparison with others.
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37. Criticism
 - In experiment , the researchers removed “Kowari “ engine
because the cannot install it.
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38. Questions?
Thank you
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