General introduction to Linked Open Data and schema extraction using SchemEX. Download full slide set to enjoy all animations.

1. SchemEX – Building an Index
for Linked Open Data
Ansgar Scherp, Thomas Gottron, Mathias Konrath
University of Koblenz-Landau, Germany
Oslo, Norway
August 2012
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2. Learning Goals
• Understand the motivation and
fundamentals of Linked Open Data (LOD).
• Qualify in why an index for LOD is needed
and how to efficiently create such an index.
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3. Scenario
• Tim plans to travel
– from London
– to a customer in Cologne
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4. Website of the German Railway
It works, why bother…?
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5. Let„s Try Different Queries
 Bottlenecks in public transportation?
 Compare the connections with flights?
 Visualize on a map?
…
 All these queries cannot be answered,
because the data …
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6. … locked in Silos!
– High Integration Effort
– Lack in Reuse of Data
SchemEX – Building an Index for LOD Slide 6Jagendorf, http://www.flickr.com/photos/bobjagendorf/, CC-BY
B. of 44

7. Linked Data
• Publishing and interlinking of data
• Different quality and purpose
• From different sources in the Web
World Wide Web Linked Data
Documents Data
Hyperlinks Typed Links
HTML RDF
Addresses (URIs) Addresses (URIs)
Example: http://www.uio.no/
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8. Relevance of Linked Data?
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9. Linked Data: May „07  Sept. „11
Web 2.0
Media
Publications
eGovernment
Cross-Domain
Life
Geographic Sciences
SchemEX – Building an Index for LOD
< 31 Billion Triples Slide 9 of 44 Source: http://lod-cloud.net

10. Linked Data Principles
1. Identification
2. Interlinkage
3. Dereferencing
4. Description
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11. Example: Big Lynx
Matt Briggs
Scott Miller
?
Big Lynx
Company
SchemEX – Building an Index
< 31 Milliarde Triple for LOD Slide 11 of 44 Source: http://lod-cloud.net

12. 1. Use URIs for Identification
Matt Briggs
Scott Miller
http://biglynx.co.uk/
people/matt-briggs
http://biglynx.co.uk/
people/scott-miller
SchemEX – Building B. Gazen,http://www.flickr.com/photos/bayat/,12 of 44
an Index for LOD Slide CC-BY

13. Example: Big Lynx
Matt Briggs
Scott Miller
Big Lynx
Company
 How to model relationships like knows?
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14. Resource DescriptionFramework (RDF)
• Description of Ressources with RDF triple
Matt Briggs is a Person
Subject Predicate Object
@prefix rdf:<http://w3.org/1999/02/22-rdf-
syntax-ns#> .
@prefix foaf:<http://xmlns.com/foaf/0.1/> .
<http://biglynx.co.uk/people/matt-briggs>
rdf:type foaf:Person .
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15. 1. Use URIs also for Relations
http://biglynx.co.uk/
people/matt-briggs
http://biglynx.co.uk/
people/scott-miller
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an Index for LOD Slide CC-BY

16. Example: Big Lynx
Dave Smith
London
„lives here―
Matt Briggs
„same
Scott Miller
Big Lynx
… person―
Company
DBpedia Matt Briggs
Matts private
Webseite
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17. 2. Establishing Interlinkage
• Relation links between ressources
<http://biglynx.co.uk/people/dave-smith>
foaf:based_near
<http://dbpedia.org/resource/London> .
 Identity links between ressources
<http://biglynx.co.uk/people/matt-briggs>
owl:sameAs
<http://www.matt-briggs.eg.uk#me> .
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18. Example: Big Lynx
Dave Smith
London
„lives here―
foaf:based_near
Matt Briggs
„same
owl:sameAs
Person― Big Lynx
Company
DBpedia Matt Briggs
Matts private
Webseite
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19. 3. Dereferencing of URIs
• Looking up of web documents
• How can we ―look up‖ things of the real world?
http://biglynx.co.uk/
people/matt-briggs
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20. Two Approaches
1. Hash URIs
– URI contains a part separated by #, e.g.,
http://biglynx.co.uk/vocab/sme#Team
2. Negotiation via „303 See Other― request
http://biglynx.co.uk/people/matt-briggs
Response: „Look here:―
http://biglynx.co.uk/people/matt-briggs.rdf
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21. Example: Big Lynx
Dave Smith
London
foaf:based_near
Description of
Matt Briggs
Matt?
owl:sameAs
Big Lynx
Company
DBpedia Matt Briggs
Matts private
Webseite
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22. 4. Description of URIs
foaf:Person …
… dp:Birmingham
rdf:type
foaf:based_near …
biglynx:matt-briggs ex:loc
_:point
foaf:knows
wgs84:
wgs84: long
biglynx:dave-smith
lat
―-0.118‖
foaf:based_near
―51.509‖
dp:London
… …
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23. RDF / RDF Schema Vocabulary
• Set of URIs defined in rdf:/rdfs: namespace
• rdf:type • rdfs:domain
• rdf:Property • rdfs:range
• rdf:XMLLiteral • rdfs:Resource
• rdf:List • rdfs:Literal
• rdf:first • rdfs:Datatype
• rdf:rest • rdfs:Class
• rdf:Seq • rdfs:subClassOf
• rdf:Bag • rdfs:subPropertyOf
• rdf:Alt • rdfs:comment
• ... • …
• rdf:value • rdfs:label
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24. Semantic Web Layer Cake (Simplified)
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25. Learning Goals
• Understand the motivation and
fundamentals of Linked Open Data (LOD).
• Qualify in why an index for LOD is needed
and how to efficiently create such an index.
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26. Scenario
• People who are politicians and actors
• Who else?
• Where do they live?
• Whom do they know? …are they married with?
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27. Problem
• No single federated query interface provided
• Execute those queries on the LOD cloud
SELECT ?x
FROM …
WHERE {
?x rdf:type ex:Actor .
?x rdf:type ex:Politician .
}
“politicians
and actors”
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28. Principle Solution
• Suitable index structure for looking up sources
“politicians
and actors”
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29. The Naive Approach
1. Download the entire LOD cloud
2. Put it into a (really) large triple store
3. Process the data and extract schema
4. Provide lookup
- Big machinery
- Late in processing the data
- High effort to scale with LOD cloud
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30. Idea
 Schema-level index
 Define families of graph patterns
 Assign instances to graph patterns
 Map graph patterns to context (source URI)
 Construction
 Stream-based for scalability
 Little loss of accuracy
 Note
 Index defined over instances
 But stores the context
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31. Input Data
 n-Quads
<subject> <predicate> <object> <context>
 Example:
<http://www.w3.org/People/Connolly/#me>
<http://www.w3.org/1999/02/22-rdf-syntax-ns#
<http://xmlns.com/foaf/0.1/Person>
<http://dig.csail.mit.edu/2008/webdav/timbl/
http://dig.csail.mit.edu/2008/
webdav/timbl/foaf.rdf
w3p:
#me
foaf:
Person
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32. Building the Schema and Index
RDF
C1 C2 C3 … Ck
classes
consistsOf
Type
TC1 TC2 … TCm clusters
hasEQ
Class p1 p2
EQC1 EQC2 … EQCn Equivalence
classes
hasDataSource
… Data
DS1 DS2 DS3 DS4 DS5 DSx sources
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33. Layer 1: RDF Classes
 All instances of a C1
particular type
DS 1 DS 2 DS 3
SELECT ?x
FROM …
WHERE {
?x rdfs:type foaf:Person .
foaf:Person
}
http://dig.csail.mit.edu/2008/...
foaf:
timbl: Person
card#i http://www.w3.org/People/Berners-Lee/card
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34. Layer 2: Type Clusters
 All instances belonging C1 C2
to exactly the same set
TC1
of types
SELECT ?x DS 1 DS 2 DS 3
FROM …
WHERE {
foaf:Person pim:Male
?x rdfs:type foaf:Person .
?x rdfs:type pim:Male . tc4711
}
pim:
Male
http://www.w3.org/People/Berners-Lee/card
foaf:
timbl:
Person
card#i
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35. Layer 3: Equivalence Classes
 Two instances are C1 C2 C3
equivalent iff:
 They are in the same TC TC1 TC2
 They have the same p
properties
EQC1
 The property targets are
in the same TC DS 1 DS 2 DS 3
 Similar to 1-Bisimulation
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36. Layer 3: Equivalence Classes
SELECT ?x
WHERE {
?x rdfs:type foaf:Person foaf:Person
.
?x rdfs:type pim:Male . pim:Male foaf:PPD
?x foaf:maker ?y .
?y rdfs:type
foaf:PersonalProfileDocument .
tc4711 tc1234
} eqc0815
-maker-
pim: foaf: foaf: tc1234
Male Person PPD
eqc0815
foaf:maker
timbl: http://www.w3.org/People/Berners-Lee/card
timbl: card
card#i
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37. The SchemEX Approach
• Stream-based schema extraction
• While crawling the data
FIFO
LOD-Crawler Instance-
RDF-Dump Cache RDF
Triple Store RDBMS
NxParser
Nquad- Schema- Schema-
Parser
Stream Extractor Level
Index
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38. Building the Index from a Stream
 Stream of n-quads (coming from a LD crawler)
… Q16, Q15, Q14, Q13, Q12, Q11, Q10, Q9, Q8, Q7, Q6, Q5, Q4, Q3, Q2, Q1
FiFo
1
C3 4
6
C2 3
4
2
C2 2
1 3
C1 5
• Linear runtime complexity wrt # of input triples
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39. Computing SchemEX: TimBL Data Set
• Analysis of a smaller data set
• 11 M triples, TimBL’s FOAF profile
• LDspider with ~ 2k triples / sec
• Different cache sizes: 100, 1k, 10k, 50k, 100k
• Compared SchemEX with reference schema
• Index queries on all Types, TCs, EQCs
• Good precision/recall ratio at 50k+
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40. Quality of Stream-based Index
Construction
• Runtime increases hardly with window size
• Memory consumption scales with window size
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41. Computing SchemEX: Full BTC 2011 Data
Cache size: 50 k
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42. Billion Triple Challenge 2011
…
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43. Conclusions: SchemEX
• Linked Open Data (LOD) approach
• Publishing and interlinking data on the web
• SchemEX
• Stream-based approach to LOD schema
extraction
• Scalable to arbitrary amount of Linked Data
• Applicable on commodity hardware
(4GB RAM, single CPU)
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44. Learning Goals
• Understand the motivation and
fundamentals of Linked Open Data (LOD).
• Qualify in why an index for LOD is needed
and how to efficiently create such an index.
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45. Recommended Readings
• Maciej Janik, Ansgar Scherp, Steffen Staab: The Semantic Web:
Collective Intelligence on the Web. Informatik Spektrum 34(5): 469-483
(2011)
URL: http://dx.doi.org/10.1007/s00287-011-0535-x
• Mathias Konrath, Thomas Gottron, Steffen Staab, Ansgar Scherp:
SchemEX — Efficient construction of a data catalogue by stream-based
indexing of linked data, J. of Web Semantics: Science, Services and
Agents on the World Wide Web, Available online 23 June 2012
URL: http://www.sciencedirect.com/science/article/pii/S1570826812000716
• Tom Heath, Christian Bizer: Linked Data: Evolving the Web into a Global
Data Space, Morgan & Claypool Publishers, 2011
URL: http://dx.doi.org/10.2200/S00334ED1V01Y201102WBE001
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