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EU-FP7 LOD2 Project Overview . Page 1 http://lod2.eu
Creating Knowledge out of Interlinked Data
http://lod2.eu
Advanced Exploration
of Public Procurement Data
in Linked Data Paradigm
Krzysztof Wecel - I2G / Poznan University of Economics
Vojtech Svatek, Jindrich Mynarz – University of Economics,
Prague
EU-FP7 LOD2 Project Slide 2 http://lod2.eu
Creating Knowledge out of Interlinked Data
Starting point:
 potential benefits for a wide range of players
 mandatory publication of public contract notices
Weak points:
 restrictions of search interfaces
 GUI in local languages
 no wider analyses available: aggregations, trends, patterns…
 lack of mechanical reasoning
 geographical context not leveraged
 no links to external information
Opportunity:
 representation in a form of linked data
Motivation
EU-FP7 LOD2 Project Slide 3 http://lod2.eu
Creating Knowledge out of Interlinked Data
Public Contracts Ontology
pc:Contra
ct
pc:Notice
gr:BusinessEntity
vc:VCard
pc:Tender
gr:Offering
gr:PriceSpecification
pc:AwardCriteriaCombination
kind
procedure
CPV
EU-FP7 LOD2 Project Slide 4 http://lod2.eu
Creating Knowledge out of Interlinked Data
Public contract notices:
 HTML – navigation, scrapping
 XML – modeling approach
 mapping issues
Additional data – the real value
 business entities (ARES, CEIDG) and their codes (ICO, NIP, REGON)
 geographical codes (NUTS, TERYT)
 geographical coordinates (geocoding)
 CPV and other vocabularies
 optional external information
• Czech Trade Inspection Authority
• sentences of Polish National Board of Appeal
Data sources and transformation
EU-FP7 LOD2 Project Slide 5 http://lod2.eu
Creating Knowledge out of Interlinked Data
number of triples: 28,8M
notices: 413,382
offerings: 922,038
contracting authorities: 17,648
contractors: 177,136
business entities: 194,784
unique CPV codes: 11,341
Polish dataset characteristics (2013)
EU-FP7 LOD2 Project Slide 6 http://lod2.eu
Creating Knowledge out of Interlinked Data
Polish dataset characteristics (2013)
EU-FP7 LOD2 Project Slide 7 http://lod2.eu
Creating Knowledge out of Interlinked Data
leverage analytical and data mining techniques in order to find patterns,
trends and anomalies in public contract data
Specific problems of graph data:
• multidimensionality
• big number of potential attributes
• overlap of the classes
• unbalanced learning data (different counts of classes)
• loss of information during transformation from graph to tabular data
Data mining
EU-FP7 LOD2 Project Slide 8 http://lod2.eu
Creating Knowledge out of Interlinked Data
beneficiary: bidder
case:
• identify contracts from the past that would be most suitable
• monitor new notices similar the contracts they have already realised
• more expressive than typical search language
beneficiary: contracting authority
case:
• help in preparation of specific contract notice (not only CPV)
• aggregated demand opportunities
CLUSTERING: looking for similar contracts
EU-FP7 LOD2 Project Slide 9 http://lod2.eu
Creating Knowledge out of Interlinked Data
beneficiary: supervisory bodies
case:
• discover anomalies
• contractor-product association: stability of the offer; the tighter the
relationship, the more reliable the contractor is
• contractor-authority association: signals the need to check for corruption
• analysis of the depth of the market
ASSOCIATIONS: ties between various market players
EU-FP7 LOD2 Project Slide 10 http://lod2.eu
Creating Knowledge out of Interlinked Data
leverages the link between notices concerning the same procurement
process
beneficiary: contracting authority
case:
• the bigger number of bidders, the better
• one bidder can mean overspecified contract notice
PREDICTIVE MODELS: number of bidders
EU-FP7 LOD2 Project Slide 11 http://lod2.eu
Creating Knowledge out of Interlinked Data
Geography vs. value association
EU-FP7 LOD2 Project Slide 12 http://lod2.eu
Creating Knowledge out of Interlinked Data
Number of tenders - long tail
• 38% of contracts had just one offer
• one bidder = overspecified contract notice?
• one contract had 610 offers
• in some cases large numbers of rejected offers: 298, 245, 111
1
10
100
1000
10000
100000
1000000
1 10 100 1000
count
count
Number of
Tenders
count Percentage
1 129910 38,2%
2 67929 20,0%
3 46412 13,6%
4 29850 8,8%
5 19367 5,7%
6 12679 3,7%
7 8277 2,4%
8 5639 1,7%
9 3920 1,2%
10 2799 0,8%
EU-FP7 LOD2 Project Slide 13 http://lod2.eu
Creating Knowledge out of Interlinked Data
Type of contract vs. number of bidding entities
• supply contracts are the most popular
• construction work was the least popular
• areas of low competitiveness are more susceptible to abuse
Number of contractors to number of notices ratio
• should be compared to typical value in similar contracting authorities
group
Some other discoveries
EU-FP7 LOD2 Project Slide 14 http://lod2.eu
Creating Knowledge out of Interlinked Data
Public procurement in Czech Republic and Poland
• ontology has been elaborated
• significant amount of data gathered
• we are looking for other interested parties
Data mining
specific issues of graph data have to be addressed
old and new tools applied
• similar contracts by clustering
• ties between various market players
• prediction of the number of bidders
Conclusions

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EDF2014: Talk of Krzysztof Wecel, Assistant professor, Poznan University of Economics, Poland: Advanced Exploration of Public Procurement Data in Linked Data Paradigm

  • 1. EU-FP7 LOD2 Project Overview . Page 1 http://lod2.eu Creating Knowledge out of Interlinked Data http://lod2.eu Advanced Exploration of Public Procurement Data in Linked Data Paradigm Krzysztof Wecel - I2G / Poznan University of Economics Vojtech Svatek, Jindrich Mynarz – University of Economics, Prague
  • 2. EU-FP7 LOD2 Project Slide 2 http://lod2.eu Creating Knowledge out of Interlinked Data Starting point:  potential benefits for a wide range of players  mandatory publication of public contract notices Weak points:  restrictions of search interfaces  GUI in local languages  no wider analyses available: aggregations, trends, patterns…  lack of mechanical reasoning  geographical context not leveraged  no links to external information Opportunity:  representation in a form of linked data Motivation
  • 3. EU-FP7 LOD2 Project Slide 3 http://lod2.eu Creating Knowledge out of Interlinked Data Public Contracts Ontology pc:Contra ct pc:Notice gr:BusinessEntity vc:VCard pc:Tender gr:Offering gr:PriceSpecification pc:AwardCriteriaCombination kind procedure CPV
  • 4. EU-FP7 LOD2 Project Slide 4 http://lod2.eu Creating Knowledge out of Interlinked Data Public contract notices:  HTML – navigation, scrapping  XML – modeling approach  mapping issues Additional data – the real value  business entities (ARES, CEIDG) and their codes (ICO, NIP, REGON)  geographical codes (NUTS, TERYT)  geographical coordinates (geocoding)  CPV and other vocabularies  optional external information • Czech Trade Inspection Authority • sentences of Polish National Board of Appeal Data sources and transformation
  • 5. EU-FP7 LOD2 Project Slide 5 http://lod2.eu Creating Knowledge out of Interlinked Data number of triples: 28,8M notices: 413,382 offerings: 922,038 contracting authorities: 17,648 contractors: 177,136 business entities: 194,784 unique CPV codes: 11,341 Polish dataset characteristics (2013)
  • 6. EU-FP7 LOD2 Project Slide 6 http://lod2.eu Creating Knowledge out of Interlinked Data Polish dataset characteristics (2013)
  • 7. EU-FP7 LOD2 Project Slide 7 http://lod2.eu Creating Knowledge out of Interlinked Data leverage analytical and data mining techniques in order to find patterns, trends and anomalies in public contract data Specific problems of graph data: • multidimensionality • big number of potential attributes • overlap of the classes • unbalanced learning data (different counts of classes) • loss of information during transformation from graph to tabular data Data mining
  • 8. EU-FP7 LOD2 Project Slide 8 http://lod2.eu Creating Knowledge out of Interlinked Data beneficiary: bidder case: • identify contracts from the past that would be most suitable • monitor new notices similar the contracts they have already realised • more expressive than typical search language beneficiary: contracting authority case: • help in preparation of specific contract notice (not only CPV) • aggregated demand opportunities CLUSTERING: looking for similar contracts
  • 9. EU-FP7 LOD2 Project Slide 9 http://lod2.eu Creating Knowledge out of Interlinked Data beneficiary: supervisory bodies case: • discover anomalies • contractor-product association: stability of the offer; the tighter the relationship, the more reliable the contractor is • contractor-authority association: signals the need to check for corruption • analysis of the depth of the market ASSOCIATIONS: ties between various market players
  • 10. EU-FP7 LOD2 Project Slide 10 http://lod2.eu Creating Knowledge out of Interlinked Data leverages the link between notices concerning the same procurement process beneficiary: contracting authority case: • the bigger number of bidders, the better • one bidder can mean overspecified contract notice PREDICTIVE MODELS: number of bidders
  • 11. EU-FP7 LOD2 Project Slide 11 http://lod2.eu Creating Knowledge out of Interlinked Data Geography vs. value association
  • 12. EU-FP7 LOD2 Project Slide 12 http://lod2.eu Creating Knowledge out of Interlinked Data Number of tenders - long tail • 38% of contracts had just one offer • one bidder = overspecified contract notice? • one contract had 610 offers • in some cases large numbers of rejected offers: 298, 245, 111 1 10 100 1000 10000 100000 1000000 1 10 100 1000 count count Number of Tenders count Percentage 1 129910 38,2% 2 67929 20,0% 3 46412 13,6% 4 29850 8,8% 5 19367 5,7% 6 12679 3,7% 7 8277 2,4% 8 5639 1,7% 9 3920 1,2% 10 2799 0,8%
  • 13. EU-FP7 LOD2 Project Slide 13 http://lod2.eu Creating Knowledge out of Interlinked Data Type of contract vs. number of bidding entities • supply contracts are the most popular • construction work was the least popular • areas of low competitiveness are more susceptible to abuse Number of contractors to number of notices ratio • should be compared to typical value in similar contracting authorities group Some other discoveries
  • 14. EU-FP7 LOD2 Project Slide 14 http://lod2.eu Creating Knowledge out of Interlinked Data Public procurement in Czech Republic and Poland • ontology has been elaborated • significant amount of data gathered • we are looking for other interested parties Data mining specific issues of graph data have to be addressed old and new tools applied • similar contracts by clustering • ties between various market players • prediction of the number of bidders Conclusions