Exploring Linked Data content through network analysis

Exploring Linked Data content
through network analysis
Christophe Guéret (@cgueret)
Free University Amsterdam

Co-explorers: Stefan Schlobach, Shenghui Wang,
Paul Groth, Frank van Harmelen

http://latc-project.eu http://www.vu.nl

Outline of the talk
What is Linked Data?

What is there is to be analysed?

Do we miss something?

New research directions and first results

November 23, 2011 Analysis of Linked Data 2/35

Linked Data (aka Semantic Web)

Linked Data

http://www.flickr.com/photos/erikcharlton/3337465138

What is the problem?
Frank and Christophe publish some open data
Roi wants to combine and enrich it

Kennissen Staad
Christophe Amsterdam
Peter Barcelona WWW
Frank David Parijs

Ville Pays Roi
Barcelone Espagne
Paris France WWW

Christophe Amsterdam Pays-Bas

Marvel icons: mermer, DeviantArt

What is the problem?
Kennissen Staad Ville Pays
Christophe
Peter
David
Amsterdam
Barcelona
Parijs
+
Barcelone
Paris
Amsterdam
Espagne
France
Pays-Bas
= ?
Data integration issue
“Kennissen”, “Staad”, “Ville”, “Pays” ?
“Paris” = “Parijs” ?
“Amsterdam” = “Amsterdam” ?

Lot of work, must be done again on updates

A solution
Do data integration at the data level

Use, and re-use, unambiguous identifiers

Use meta-level descriptions of the identifiers

Proposal: use the Web as a platform
Identifiers = URIs
Descriptions = de-referenced documents

Frank publishes his data Kennissen Staad
Christophe Amsterdam
Peter Barcelona
This is a “triple”
David Parijs
ex:Acquaintance

rdf:type rdf:type rdf:type

ex:Christophe ex:Peter ex:David

ex:worksIn ex:worksIn ex:worksIn

dbpedia:Amsterdam dbpedia:Barcelona dbpedia:Paris

Use of compact URIs
dbpedia = http://dbpedia.org/resource/
ex = http://example.org/
rdf = http://www.w3.org/1999/02/22-rdf-syntax-ns#


Christophe re-use part of Frank's data Ville Pays
to publish his data Barcelone Espagne
Paris France
Amsterdam Pays-Bas
ex:Acquaintance





ex:isIn ex:isIn ex:isIn

dbpedia:Netherlands dbpedia:Spain dbpedia:France


Roi add some “Conocido”@es
more information
rdf:label

ex:Acquaintance








dbpedia:Europe


dbpedia:Amsterdam


Reasoning with Semantics Bonus!

dbpedia:Amsterdam ex:isIn dbpedia:Amsterdam

ex:isIn rdf:type

dbpedia:Netherlands + owl:TransitiveProperty = ex:isIn

ex:isIn

dbpedia:Europe dbpedia:Europe

Example usage
Materialize implicit information
Check for consistency

Rough estimate of size
295 data sets, 31B facts in LOD Cloud

Linking Open Data cloud diagram, by Richard Cyganiak and Anja Jentzsch. http://lod-cloud.net/

Lots of Data to analyze! :-)

http://www.flickr.com/photos/argonne/3323018571

But analyzing what exactly?
Table of facts published at different locations
A distributed Knowledge Base
Subject Predicate Object
ex:Christophe rdf:type ex:Acquaintance
ex:Christophe ex:worksIn dbpedia:Amsterdam
ex:Peter rdf:type ex:Acquaintance
... ... ...
dbpedia:Amsterdam ex:isIn dbpedia:Netherlands
dbpedia:Netherlands ex:isIn dbpedia:Europe
... ... ...
ex:Acquaintance rdf:label “Conocido”@es
... ... ...

Analysis workflow
1.Gather a snapshot of triples
2.Compute descriptive statistics
Top resources (subject, predicate, object)
Frequency cross-links types (SP,SO,PO,...)
Connected components
Paths frequency
…

=> Tricky enough, the data is really big!
=> We should be able to get more out of the data

Can we explain that?

Suggestions
Started the graph
General knowledge
Very well known


or that?

Suggestions
All published by Bio2RDF
Well aware of each other
Overlapping domain


Could we predict the impact of ...
Dbpedia being down for a while ?

SIOC renaming “User” into “UserAccount” ?

creating a dataset that turns out to be popular ?

Analysing a set of triples is not enough

Are we overlooking something?


It's not only about the resources
Several entities related to the data
ex:something WWW

Data publishers/consumers Resources Web servers

Interactions between all of them

WWW


There are different scales
Triples level versus Resource groups level
Different data complexity at each scale
“Conocido”@es

rdf:label

ex:Acquaintance








dbpedia:Europe


It is not a static network
Size and topology evolve over time

2007 2008 2010


Linked Data is a Complex System
Multiple scale of observation
Emergence of properties
The whole is more than the sum of the parts

=> Interactions/relations are important to
understand the system behavior

=> We can benefit from a large body of
research results in Complex Systems study

Initial findings and future work

Ya3hs3/2531493704 on Flickr

New analysis workflow
1.Gather a snapshot of triples

2.Gather information about other type of interactions

3.Create specific networks related to the research
questions at hand

4.Run metrics, interpret results


The LOD is not what we think it is
LOD Cloud 2009/2010 vs BTC 2009 crawl
Crawled sample differs from the community
based view

LOD Cloud has lumpy structure

Evolution of LOD Cloud
centrality changes
Increased density and connectivity
Christophe Guéret, Shenghui Wang, Paul Groth et al. (2011)
Multi-scale Analysis of the Web Of Data: A Challenge to the Complex System's Community
Advances in Complex Systems 14 (04)

The tools we need don't exist
We need to flatten the networks to study them

Some specific aspects of the system
Existence of implicit links
Multi-relational and dynamic
Distributed
Hypergraph of relations

Christophe Guéret, Shenghui Wang, Paul Groth et al. (2011)
Multi-scale Analysis of the Web Of Data: A Challenge to the Complex System's Community
Advances in Complex Systems 14 (04)

Influence content<->social networks
Generate and bind two networks
ex:a

ex:b

ex:c

Measure evolution of degree, betweenness,
clustering over time
Predict evolution Shenghui Wang, Paul Groth (2010)
Measuring the dynamic bi-directional influence between content and social networks
Proceedings of the 9th International Semantic Web Conference (ISWC2010)

Result for conferences

Shenghui Wang, Paul Groth (2010)
Measuring the dynamic bi-directional influence between content and social networks

Centrality to measure robustness
Map the BTC2010 to two networks
Semantic network based on namespaces
Host networks based on hostnames

Measure robustness as the variance in betweenness
centrality

Find weak spots

Optimize networks to increase robustness
Christophe Guéret, Paul Groth, Frank Van Harmelen et al. (2010)
Finding the Achilles Heel of the Web of Data : using network analysis for link-recommendation

Results on hostnames


Results on namespaces


Improving the network


Conclusion
Take home message
Linked Data is not a simple knowledge base
Network analysis tools give new insights on the data
Results can be used to improve the network

Future work
Make resource-centric analysis rather than graph-
centric analysis (big bottleneck now)
Tackle the time aspect of the data
Find more analysis to perform and what they tell us


Exploring Linked Data content through network analysis

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