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Linked Open Data enhanced
Knowledge Discovery
Introducing the RapidMiner
Linked Open Data Extension
Heiko Paulheim
09/30/15 Heiko Paulheim 2
The Web is Full of Data...
09/30/15 Heiko Paulheim 3
Motivating Example
• Understanding population changes in the Netherlands
09/30/15 Heiko Paulheim 4
Motivating Example
• Understanding population changes in the Netherlands
• What we can see in the data
– population changes by municipality are very diverse
– ranging from -12% to +53% over the last 15 years
• What we cannot see from the data
– How do growing regions differ from
shrinking ones?
– Which factors drive people's movements?
• As very often, we need more knowledge...
09/30/15 Heiko Paulheim 5
Motivating Example
• Proposed approach:
– link data at hand to LOD Cloud
– harvest additional information about regions
– look for interesting patterns
link combine cleanse transform analyze
09/30/15 Heiko Paulheim 6
RapidMiner Linked Open Data Extension
Introducing RapidMiner:
●
An open source platform for data mining and predictive analytics
●
Processes are designed by wiring operators in a GUI
(no programming)
●
Operators for data loading, transformation, modeling, visualization, …
●
Scalable, distributed, parallel processing in a cloud environment
●
200,000 active users
●
Developers can write their own extensions
09/30/15 Heiko Paulheim 7
RapidMiner Linked Open Data Extension
• The extension adds operators for
– accessing local and remote (Linked and non Linked) data
– linking local to remote data
– combining data from various sources
– automatically following links to other datasets
• Data analysts can use it without knowing RDF, SPARQL, etc.
09/30/15 Heiko Paulheim 8
Example Use Case
• Understanding population changes in the Netherlands
• RapidMiner workflow:
– Import original table
– Link municipalities to DBpedia
• alternative: link provinces to Eurostat
– Build enriched table
– Analyze the results
09/30/15 Heiko Paulheim 9
Example Findings
• Growing regions: Flevoland, Utrecht, North/South Holland
• Shrinking regions: Limburg, Groningen, Friesland
• Provincal capitals are growing
• Growth in regions with high population
• Growth in regions with high income
– but also: growth in regions with high unemployment
09/30/15 Heiko Paulheim 10
Example Findings
• Negative correlation between growth and elevation?!
09/30/15 Heiko Paulheim 11
Behind the Scenes: RapidMiner LOD Extension
• RapidMiner uses a tabular data model
09/30/15 Heiko Paulheim 12
Behind the Scenes: RapidMiner LOD Extension
• Linking local data to LOD Sources
– based on URI patterns
– based on text search
– using specialized services (e.g., DBpedia Lookup)
• Following links
– e.g., automatically follow all owl:sameAs links to other datasets
to a certain depth
• Harvesting attributes
– e.g., add all numeric attributes found
– built-in support for aggregations
09/30/15 Heiko Paulheim 13
Behind the Scenes: RapidMiner LOD Extension
• Matching and fusion
– e.g., many sources contain “population”
as an attribute
– automatic identification of similar attributes
– automatic fusion using different policies
• Attribute set filtering
– exploiting schema information
– more effective in finding redundant attributes
09/30/15 Heiko Paulheim 14
Full RapidMiner Workflow for the Example
09/30/15 Heiko Paulheim 15
Other Examples
• Analyzing unemployment in France (SemStats'13)
– using background knowledge from DBpedia, Eurostat, Linked Geo Data
– exploiting links from DBpedia to GADM for visualization
09/30/15 Heiko Paulheim 16
Other Examples
• Example correlations for unemployment in France:
– African islands, Islands in the Indian Ocean,
Outermost regions of the EU (positive)
– GDP (negative)
– Disposable income (negative)
– Hospital beds/inhabitants (negative)
– RnD spendings (negative)
– Energy consumption (negative)
– Population growth (positive)
– Casualties in traffic accidents (negative)
– Fast food restaurants (positive)
– Police stations (positive)
09/30/15 Heiko Paulheim 17
Other Examples
• Data Set: Suicide rates by country
– http://www.washingtonpost.com/wp-srv/world/suiciderate.html
• Findings for suicide rates
– Democracies have lower suicide rates than other forms of government
– High HDI → low suicide rate
– High population density → high suicide rate
– By geography:
• At the sea → low
• In the mountains → high
– High Gini index → low suicide rate
• High Gini index ↔ unequal distribution of wealth
– High usage of nuclear power → high suicide rates
09/30/15 Heiko Paulheim 18
Other Examples
• Data set: Durex worldwise survey on sexual activity
– http://chartsbin.com/view/uya
• Findings:
– By geography:
• High in Europe, low in Asia
• Low in Island states
– By language:
• English speaking: low
• French speaking: high
– Low average age → high activity
– High GDP per capita → low activity
– High unemployment rate → high activity
– High number of ISP providers → low activity
09/30/15 Heiko Paulheim 19
Caveat
• We have only been analyzing correlations here.
09/30/15 Heiko Paulheim 20
Other Use Cases
• Incident detection from Twitter
fire at #mannheim
#universityomg two cars on
fire #A5 #accident
fire at train station
still burning
my heart
is on fire!!!come on baby
light my fire
boss should fire
that stupid moron
09/30/15 Heiko Paulheim 21
Other Use Cases
• Example set:
– “Again crash on I90”
– “Accident on I90”
dbpedia:Interstate_90
dbpedia-owl:Road
rdf:type
dbpedia:Interstate_51
rdf:type
• Model:
– dbpedia-owl:Road → indicates traffic accident
• Applying the model:
– “Two cars collided on I51” → indicates traffic accident
• Using LOD+RapidMiner
– automatically learns a model
– avoids overfitting
09/30/15 Heiko Paulheim 22
Other Use Cases
• Building Semantic Recommeder Systems (ESWC'14)
• Combines two extensions:
– Linked Open Data extension
– Recommender system extension
• Use data about books
for content-based recommender
– best system (out of 24)
on two out of three tasks
– used data from DBpedia
and RDF Book Mashup
09/30/15 Heiko Paulheim 23
What is Special about Hilversum?
• Compare Hilversum to other Cities in the Netherlands
– find distinctive features
• Finding the needles in the haystack
of statements about Hilversum
in DBpedia
09/30/15 Heiko Paulheim 24
What is Special about Hilversum?
• Compare Hilversum to other Cities in the Netherlands
– find distinctive features
09/30/15 Heiko Paulheim 25
What is Special about Hilversum?
• Compare Hilversum to other Cities in the Netherlands
– find distinctive features
• TopFacts application
– Demonstration at ISWC 2015
– Combines Linked Open Data with attribute-wise outlier detection
[see Paulheim/Meusel, Machine Learning 100(2-3), 2015]
09/30/15 Heiko Paulheim 26
What is Special about Hilversum?
• Compare Hilversum to other Cities in the Netherlands
– find distinctive features
• Hilversum is
– a city where the modern Pentathlon olympics have been held
– the headquarter of many media companies
– a place where many music recordings have been made
09/30/15 Heiko Paulheim 27
Other Use Cases
• Debugging Linked Open Data
– loading a set of statements
– augment with additional features
– run outlier detection
• again: a special
extension
• Example: identify wrong
dataset interlinks
(WoDOOM'14)
– AUC up to 85%
09/30/15 Heiko Paulheim 28
Summary
• The RapidMiner LOD Extension
– brings data analysis to the web of data
– can be used by data analysts without learning SPARQL
• Availability
– on the RapidMiner
marketplace
– installable from
inside RapidMiner
– >9,000 installations
and counting
09/30/15 Heiko Paulheim 29
Take Home Messages
• The Web is full of data
– ...and more and more becomes Linked Data
• Intelligent data processing
– helps unlocking the potential of that data
– enables intelligent applications
• A good fit
– Sophisticated analytics platforms
(e.g., RapidMiner), and
– Linked Open Data
09/30/15 Heiko Paulheim 30
Feedback?
Heiko Paulheim
heiko@informatik.uni-mannheim.de
@heikopaulheim
Linked Open Data enhanced
Knowledge Discovery
Introducing the RapidMiner
Linked Open Data Extension
Heiko Paulheim

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Linked Open Data enhanced Knowledge Discovery

  • 1. Linked Open Data enhanced Knowledge Discovery Introducing the RapidMiner Linked Open Data Extension Heiko Paulheim
  • 2. 09/30/15 Heiko Paulheim 2 The Web is Full of Data...
  • 3. 09/30/15 Heiko Paulheim 3 Motivating Example • Understanding population changes in the Netherlands
  • 4. 09/30/15 Heiko Paulheim 4 Motivating Example • Understanding population changes in the Netherlands • What we can see in the data – population changes by municipality are very diverse – ranging from -12% to +53% over the last 15 years • What we cannot see from the data – How do growing regions differ from shrinking ones? – Which factors drive people's movements? • As very often, we need more knowledge...
  • 5. 09/30/15 Heiko Paulheim 5 Motivating Example • Proposed approach: – link data at hand to LOD Cloud – harvest additional information about regions – look for interesting patterns link combine cleanse transform analyze
  • 6. 09/30/15 Heiko Paulheim 6 RapidMiner Linked Open Data Extension Introducing RapidMiner: ● An open source platform for data mining and predictive analytics ● Processes are designed by wiring operators in a GUI (no programming) ● Operators for data loading, transformation, modeling, visualization, … ● Scalable, distributed, parallel processing in a cloud environment ● 200,000 active users ● Developers can write their own extensions
  • 7. 09/30/15 Heiko Paulheim 7 RapidMiner Linked Open Data Extension • The extension adds operators for – accessing local and remote (Linked and non Linked) data – linking local to remote data – combining data from various sources – automatically following links to other datasets • Data analysts can use it without knowing RDF, SPARQL, etc.
  • 8. 09/30/15 Heiko Paulheim 8 Example Use Case • Understanding population changes in the Netherlands • RapidMiner workflow: – Import original table – Link municipalities to DBpedia • alternative: link provinces to Eurostat – Build enriched table – Analyze the results
  • 9. 09/30/15 Heiko Paulheim 9 Example Findings • Growing regions: Flevoland, Utrecht, North/South Holland • Shrinking regions: Limburg, Groningen, Friesland • Provincal capitals are growing • Growth in regions with high population • Growth in regions with high income – but also: growth in regions with high unemployment
  • 10. 09/30/15 Heiko Paulheim 10 Example Findings • Negative correlation between growth and elevation?!
  • 11. 09/30/15 Heiko Paulheim 11 Behind the Scenes: RapidMiner LOD Extension • RapidMiner uses a tabular data model
  • 12. 09/30/15 Heiko Paulheim 12 Behind the Scenes: RapidMiner LOD Extension • Linking local data to LOD Sources – based on URI patterns – based on text search – using specialized services (e.g., DBpedia Lookup) • Following links – e.g., automatically follow all owl:sameAs links to other datasets to a certain depth • Harvesting attributes – e.g., add all numeric attributes found – built-in support for aggregations
  • 13. 09/30/15 Heiko Paulheim 13 Behind the Scenes: RapidMiner LOD Extension • Matching and fusion – e.g., many sources contain “population” as an attribute – automatic identification of similar attributes – automatic fusion using different policies • Attribute set filtering – exploiting schema information – more effective in finding redundant attributes
  • 14. 09/30/15 Heiko Paulheim 14 Full RapidMiner Workflow for the Example
  • 15. 09/30/15 Heiko Paulheim 15 Other Examples • Analyzing unemployment in France (SemStats'13) – using background knowledge from DBpedia, Eurostat, Linked Geo Data – exploiting links from DBpedia to GADM for visualization
  • 16. 09/30/15 Heiko Paulheim 16 Other Examples • Example correlations for unemployment in France: – African islands, Islands in the Indian Ocean, Outermost regions of the EU (positive) – GDP (negative) – Disposable income (negative) – Hospital beds/inhabitants (negative) – RnD spendings (negative) – Energy consumption (negative) – Population growth (positive) – Casualties in traffic accidents (negative) – Fast food restaurants (positive) – Police stations (positive)
  • 17. 09/30/15 Heiko Paulheim 17 Other Examples • Data Set: Suicide rates by country – http://www.washingtonpost.com/wp-srv/world/suiciderate.html • Findings for suicide rates – Democracies have lower suicide rates than other forms of government – High HDI → low suicide rate – High population density → high suicide rate – By geography: • At the sea → low • In the mountains → high – High Gini index → low suicide rate • High Gini index ↔ unequal distribution of wealth – High usage of nuclear power → high suicide rates
  • 18. 09/30/15 Heiko Paulheim 18 Other Examples • Data set: Durex worldwise survey on sexual activity – http://chartsbin.com/view/uya • Findings: – By geography: • High in Europe, low in Asia • Low in Island states – By language: • English speaking: low • French speaking: high – Low average age → high activity – High GDP per capita → low activity – High unemployment rate → high activity – High number of ISP providers → low activity
  • 19. 09/30/15 Heiko Paulheim 19 Caveat • We have only been analyzing correlations here.
  • 20. 09/30/15 Heiko Paulheim 20 Other Use Cases • Incident detection from Twitter fire at #mannheim #universityomg two cars on fire #A5 #accident fire at train station still burning my heart is on fire!!!come on baby light my fire boss should fire that stupid moron
  • 21. 09/30/15 Heiko Paulheim 21 Other Use Cases • Example set: – “Again crash on I90” – “Accident on I90” dbpedia:Interstate_90 dbpedia-owl:Road rdf:type dbpedia:Interstate_51 rdf:type • Model: – dbpedia-owl:Road → indicates traffic accident • Applying the model: – “Two cars collided on I51” → indicates traffic accident • Using LOD+RapidMiner – automatically learns a model – avoids overfitting
  • 22. 09/30/15 Heiko Paulheim 22 Other Use Cases • Building Semantic Recommeder Systems (ESWC'14) • Combines two extensions: – Linked Open Data extension – Recommender system extension • Use data about books for content-based recommender – best system (out of 24) on two out of three tasks – used data from DBpedia and RDF Book Mashup
  • 23. 09/30/15 Heiko Paulheim 23 What is Special about Hilversum? • Compare Hilversum to other Cities in the Netherlands – find distinctive features • Finding the needles in the haystack of statements about Hilversum in DBpedia
  • 24. 09/30/15 Heiko Paulheim 24 What is Special about Hilversum? • Compare Hilversum to other Cities in the Netherlands – find distinctive features
  • 25. 09/30/15 Heiko Paulheim 25 What is Special about Hilversum? • Compare Hilversum to other Cities in the Netherlands – find distinctive features • TopFacts application – Demonstration at ISWC 2015 – Combines Linked Open Data with attribute-wise outlier detection [see Paulheim/Meusel, Machine Learning 100(2-3), 2015]
  • 26. 09/30/15 Heiko Paulheim 26 What is Special about Hilversum? • Compare Hilversum to other Cities in the Netherlands – find distinctive features • Hilversum is – a city where the modern Pentathlon olympics have been held – the headquarter of many media companies – a place where many music recordings have been made
  • 27. 09/30/15 Heiko Paulheim 27 Other Use Cases • Debugging Linked Open Data – loading a set of statements – augment with additional features – run outlier detection • again: a special extension • Example: identify wrong dataset interlinks (WoDOOM'14) – AUC up to 85%
  • 28. 09/30/15 Heiko Paulheim 28 Summary • The RapidMiner LOD Extension – brings data analysis to the web of data – can be used by data analysts without learning SPARQL • Availability – on the RapidMiner marketplace – installable from inside RapidMiner – >9,000 installations and counting
  • 29. 09/30/15 Heiko Paulheim 29 Take Home Messages • The Web is full of data – ...and more and more becomes Linked Data • Intelligent data processing – helps unlocking the potential of that data – enables intelligent applications • A good fit – Sophisticated analytics platforms (e.g., RapidMiner), and – Linked Open Data
  • 30. 09/30/15 Heiko Paulheim 30 Feedback? Heiko Paulheim heiko@informatik.uni-mannheim.de @heikopaulheim
  • 31. Linked Open Data enhanced Knowledge Discovery Introducing the RapidMiner Linked Open Data Extension Heiko Paulheim