schemex -- building an index for linked open data
DESCRIPTION
General introduction to Linked Open Data and schema extraction using SchemEX. Download full slide set to enjoy all animations.TRANSCRIPT
Slide 1 of 44SchemEX – Building an Index for LOD
SchemEX – Building an Index for Linked Open Data
Ansgar Scherp, Thomas Gottron, Mathias KonrathUniversity of Koblenz-Landau, Germany
Oslo, NorwayAugust 2012
Slide 2 of 44SchemEX – Building an Index for LOD
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.
Slide 3 of 44SchemEX – Building an Index for LOD
Scenario
• Tim plans to travel– from London – to a customer in Cologne
Slide 4 of 44SchemEX – Building an Index for LOD
Website of the German Railway
It works, why bother…?
Eurostar
DB
KVB
Slide 5 of 44SchemEX – Building an Index for LOD
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 …
Slide 6 of 44SchemEX – Building an Index for LOD
… locked in Silos!
– High Integration Effort– Lack in Reuse of Data
B. Jagendorf, http://www.flickr.com/photos/bobjagendorf/, CC-BY
Slide 7 of 44SchemEX – Building an Index for LOD
Linked Data
• Publishing and interlinking of data• Different quality and purpose• From different sources in the Web
World Wide Web Linked DataDocuments DataHyperlinks Typed LinksHTML RDFAddresses (URIs) Addresses (URIs)
Example: http://www.uio.no/
Slide 8 of 44SchemEX – Building an Index for LOD
Relevance of Linked Data?
Slide 9 of 44SchemEX – Building an Index for LOD
Linked Data: May ‘07
< 31 Billion Triples
Media
Geographic
Publications
Web 2.0
eGovernment
Cross-Domain
LifeSciences
Sept. ‘11
Source: http://lod-cloud.net
Slide 10 of 44SchemEX – Building an Index for LOD
Linked Data Principles
1. Identification2. Interlinkage3. Dereferencing4. Description
Slide 11 of 44SchemEX – Building an Index for LOD
Example: Big Lynx
Big LynxCompany
Matt Briggs
Scott Miller
Source: http://lod-cloud.net< 31 Milliarde Triple
?
Slide 12 of 44SchemEX – Building an Index for LOD
1. Use URIs for Identification
B. Gazen,http://www.flickr.com/photos/bayat/, CC-BY
http://biglynx.co.uk/people/matt-briggs
http://biglynx.co.uk/people/scott-miller
Matt Briggs
Scott Miller
Slide 13 of 44SchemEX – Building an Index for LOD
Example: Big Lynx
Big LynxCompany
Matt Briggs
Scott Miller
How to model relationships like knows?
Slide 14 of 44SchemEX – Building an Index for LOD
• Description of Ressources with RDF triple
Matt Briggs is a Person
Resource Description Framework (RDF)
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 .
Slide 15 of 44SchemEX – Building an Index for LOD
1. Use URIs also for Relations
B. Gazen,http://www.flickr.com/photos/bayat/, CC-BY
http://biglynx.co.uk/people/matt-briggs
http://biglynx.co.uk/people/scott-miller
foaf:knows
foaf:knows
Slide 16 of 44SchemEX – Building an Index for LOD
Example: Big Lynx
Big Lynx Company
DBpedia
Matts privateWebseite
„sameperson“
Matt Briggs
„lives here“
Dave SmithLondon
…
Matt Briggs
Scott Miller
Slide 17 of 44SchemEX – Building an Index for LOD
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> .
Slide 18 of 44SchemEX – Building an Index for LOD
Example: Big Lynx
DBpedia
Big Lynx Company
Matts privateWebseite
foaf:based_near
owl:sameAs
Matt Briggs
Dave SmithLondon
Matt Briggs
„same Person“
„lives here“
Slide 19 of 44SchemEX – Building an Index for LOD
• Looking up of web documents
• How can we “look up” things of the real world?
3. Dereferencing of URIs
http://biglynx.co.uk/people/matt-briggs
Slide 20 of 44SchemEX – Building an Index for LOD
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
Slide 21 of 44SchemEX – Building an Index for LOD
Example: Big Lynx
DBpedia
Big Lynx Company
Matts privateWebseite
foaf:based_near
owl:sameAs
Matt Briggs
Dave SmithLondon
Matt BriggsDescription of Matt?
Slide 22 of 44SchemEX – Building an Index for LOD
4. Description of URIs
biglynx:matt-briggs
foaf:Person
biglynx:dave-smith
dp:Birmingham
rdf:type
foaf:knows
foaf:based_near
_:point
wgs84:lat
wgs84:long
dp:London
foaf:based_near
……
…
…
ex:loc
…
“-0.118”
“51.509”
Slide 23 of 44SchemEX – Building an Index for LOD
RDF / RDF Schema Vocabulary
• rdf:type • rdf:Property • rdf:XMLLiteral • rdf:List • rdf:first • rdf:rest • rdf:Seq • rdf:Bag • rdf:Alt • ... • rdf:value
• rdfs:domain • rdfs:range • rdfs:Resource • rdfs:Literal • rdfs:Datatype • rdfs:Class • rdfs:subClassOf • rdfs:subPropertyOf • rdfs:comment • …• rdfs:label
• Set of URIs defined in rdf:/rdfs: namespace
Slide 24 of 44SchemEX – Building an Index for LOD
Semantic Web Layer Cake (Simplified)
Slide 25 of 44SchemEX – Building an Index for LOD
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.
Slide 26 of 44SchemEX – Building an Index for LOD
Scenario• People who are politicians and actors
• Who else? • Where do they live? • Whom do they know? …are they married with?
Slide 27 of 44SchemEX – Building an Index for LOD
Problem
• Execute those queries on the LOD cloud
Relevant sources?
• No single federated query interface provided
“politicians and actors”
SELECT ?xFROM …WHERE { ?x rdf:type ex:Actor . ?x rdf:type ex:Politician .}
Slide 28 of 44SchemEX – Building an Index for LOD
Principle Solution
• Suitable index structure for looking up sources
“politicians and actors”
Slide 29 of 44SchemEX – Building an Index for LOD
The Naive Approach
1. Download the entire LOD cloud2. Put it into a (really) large triple store3. Process the data and extract schema4. Provide lookup
- Big machinery- Late in processing the data- High effort to scale with LOD cloudCan we do smarter?
Can we do smarter?
Slide 30 of 44SchemEX – Building an Index for LOD
Idea Schema-level index
Define families of graph patternsAssign instances to graph patternsMap graph patterns to context (source URI)
ConstructionStream-based for scalabilityLittle loss of accuracy
Note Index defined over instancesBut stores the context
Slide 31 of 44SchemEX – Building an Index for LOD
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#type> <http://xmlns.com/foaf/0.1/Person> <http://dig.csail.mit.edu/2008/webdav/timbl/foaf.rdf>
w3p:#me
foaf:Person
http://dig.csail.mit.edu/2008/webdav/timbl/foaf.rdf
Slide 32 of 44SchemEX – Building an Index for LOD
DS1 DS2 DS3 DS4 DS5 DSxData
sources
consistsOf
hasDataSource
Building the Schema and Index
…
EQC1 EQC2 EQCn Equivalenceclasses
hasEQClass p1 p2
…
TC1 TC2 TCm
Type clusters…
C2C1 C3 CkRDF
classes…
Slide 33 of 44SchemEX – Building an Index for LOD
Layer 1: RDF Classes
timbl:card#i
foaf:Person
foaf:Person
http://www.w3.org/People/Berners-Lee/card
http://dig.csail.mit.edu/2008/...
C1
DS 3DS 2DS 1
SELECT ?xFROM …WHERE { ?x rdfs:type foaf:Person .}
All instances of a particular type
Slide 34 of 44SchemEX – Building an Index for LOD
Layer 2: Type Clusters
timbl:card#i
foaf:Person
foaf:Person
http://www.w3.org/People/Berners-Lee/card
DS 3DS 2DS 1SELECT ?xFROM …WHERE { ?x rdfs:type foaf:Person . ?x rdfs:type pim:Male .}
C1
C2
pim:Male
tc4711
pim:Male
All instances belonging to exactly the same set of types
TC1
Slide 35 of 44SchemEX – Building an Index for LOD
Two instances are equivalent iff:They are in the same TCThey have the same
propertiesThe property targets are
in the same TC
Similar to 1-Bisimulation
Layer 3: Equivalence Classes
EQC1
DS 3DS 2DS 1
C1
C2
TC1
C3
TC2
p
Slide 36 of 44SchemEX – Building an Index for LOD
Layer 3: Equivalence Classes
timbl:card#i
foaf:Person
foaf:Person
http://www.w3.org/People/Berners-Lee/card
pim:Male
tc4711
pim:Male
foaf:PPD
timbl:card
eqc0815
foaf:PPD
tc1234
eqc0815-maker-tc1234
foaf:maker
SELECT ?x WHERE { ?x rdfs:type foaf:Person . ?x rdfs:type pim:Male . ?x foaf:maker ?y . ?y rdfs:type foaf:PersonalProfileDocument .}
Slide 37 of 44SchemEX – Building an Index for LOD
The SchemEX Approach• Stream-based schema extraction• While crawling the data
Nquad-Stream
Schema-LevelIndex
Schema-Extractor
Parser
Instance-Cache
LOD-CrawlerRDF-DumpTriple Store
NxParser
RDFRDBMS
FIFO
Slide 38 of 44SchemEX – Building an Index for LOD
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
4
3
2
1
1
6
23
4
5
C3
C2
C2
C1
• Linear runtime complexity wrt # of input triples
Slide 39 of 44SchemEX – Building an Index for LOD
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+
Slide 40 of 44SchemEX – Building an Index for LOD
Quality of Stream-based Index Construction
• Runtime increases hardly with window size• Memory consumption scales with window size
Slide 41 of 44SchemEX – Building an Index for LOD
Computing SchemEX: Full BTC 2011 Data
Cache size: 50 k
Slide 42 of 44SchemEX – Building an Index for LOD
Billion Triple Challenge 2011
…
Slide 43 of 44SchemEX – Building an Index for LOD
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)
Slide 44 of 44SchemEX – Building an Index for LOD
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.
Slide 45 of 44SchemEX – Building an Index for LOD
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 2012URL: 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, 2011URL: http://dx.doi.org/10.2200/S00334ED1V01Y201102WBE001