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Linking
Government
Open Data
with Trade
Register Data
TEAM ЧЕРЕШКА (ХАКЕР)
IVA DELCHEVA
YASEN KIPROV
NIKI PETROV
VIKTOR SE...
Recap of what we did
Geographic data from Google API
http://Maps.googleapis.com
Trade Register data
http://www.brra.bg/
Op...
Data Model
Government Open Data
 :GovernmentProcurement Държавна
поръчка :Procurement_190862
 :principle Фирма възложите...
Top 5
Connected
Companies
We are exploring which are the companies, connected
to largest number of other companies. The nu...
 easy
SELECT ?c1 ?c2 ?c1_mane ?c2_mane ?no_man WHERE { ?c1
a :Company; bottom:prefLabel ?c1_mane;
:hasActiveManager ?am1....
https://docs.google.com/spreadsheets/d/1Czhewqx3Eum37JqnX54o
cFl219WEOFkYjvi8Qn2BAjU/edit?usp=sharing
People who influence both principle
and contractor
SELECT ?p_name ?p ?proc ?c_name ?c
?c2_name ?c2 ?val ?cur
WHERE {
?proc...
http://ec2-34-249-186-171.eu-west-
1.compute.amazonaws.com/sparql
Information provided above does not imply that there is ...
Visualizations
 https://public.tableau.com/profil
e/publish/ProcurementBG/Proc
urementBG2011-
2016GraphDBEIK#!/publish-
c...
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Linking Bulgarian Government Open Data with Trade Register Slide 1 Linking Bulgarian Government Open Data with Trade Register Slide 2 Linking Bulgarian Government Open Data with Trade Register Slide 3 Linking Bulgarian Government Open Data with Trade Register Slide 4 Linking Bulgarian Government Open Data with Trade Register Slide 5 Linking Bulgarian Government Open Data with Trade Register Slide 6 Linking Bulgarian Government Open Data with Trade Register Slide 7 Linking Bulgarian Government Open Data with Trade Register Slide 8 Linking Bulgarian Government Open Data with Trade Register Slide 9
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Linking Bulgarian Government Open Data with Trade Register

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These are the results of the #datathon17 challenge to create a linked open dataset of Bulgarian trade data.

Linking Bulgarian Government Open Data with Trade Register

  1. 1. Linking Government Open Data with Trade Register Data TEAM ЧЕРЕШКА (ХАКЕР) IVA DELCHEVA YASEN KIPROV NIKI PETROV VIKTOR SENDEROV
  2. 2. Recap of what we did Geographic data from Google API http://Maps.googleapis.com Trade Register data http://www.brra.bg/ Open Government Data: Procurements and European projects in principle https://opendata.government.bg/dataset/projectslist/resource/4ab9e162- a0c5-4bc2-94a6-5bdb2bbbb94b
  3. 3. Data Model Government Open Data  :GovernmentProcurement Държавна поръчка :Procurement_190862  :principle Фирма възложител :Company_000740495  :contractor Фирма изпълнител :Company_201064812 Trade Register  :hasManager*, :hadDirector*, :hasSoleCapitalOwner -> :hasInfluencingPerson Google Maps  geo-pos:lat, geo-pos:long
  4. 4. Top 5 Connected Companies We are exploring which are the companies, connected to largest number of other companies. The numbers represent that Company A is connected via common active board member to “n” other companies. They need to have at least one common board member. In order to escape false positives, we selected only companies that have board member with unique identifier – (EGN || LNCH) In order to make sure, that the linked companies are not, actually a bunch of companies owned by a single person (Like the case of Робърт Артур Скорупка), we have selected only companies with more than 3 board members *Let me know if this makes sense
  5. 5.  easy SELECT ?c1 ?c2 ?c1_mane ?c2_mane ?no_man WHERE { ?c1 a :Company; bottom:prefLabel ?c1_mane; :hasActiveManager ?am1. FILTER ( regex( str(?am1), "(_LNCH_)|(_EGN_)")) ?c2 a :Company; :hasActiveManager ?am2; bottom:prefLabel ?c2_mane; :identifier ?id2. ?c1 :hasActiveManager ?am2 MINUS {?c1 :identifier ?id2}. { SELECT ?c1 (COUNT (DISTINCT ?c2) AS ?no_con) (COUNT (DISTINCT ?am1) AS ?no_man) WHERE { ?c1 a :Company; :hasActiveManager ?am1. FILTER ( regex( str(?am1), "(_LNCH_)|(_EGN_)")) ?c2 a :Company; :hasActiveManager ?am2; :identifier ?id2. ?c1 :hasActiveManager ?am2 MINUS {?c1 :identifier ?id2} } GROUP BY ?c1 HAVING (?no_man>3) ORDER BY DESC (?no_con) LIMIT 5 } }
  6. 6. https://docs.google.com/spreadsheets/d/1Czhewqx3Eum37JqnX54o cFl219WEOFkYjvi8Qn2BAjU/edit?usp=sharing
  7. 7. People who influence both principle and contractor SELECT ?p_name ?p ?proc ?c_name ?c ?c2_name ?c2 ?val ?cur WHERE { ?proc a :GovernmentProcurement ; :principle ?c ; :contractor ?c2 ; :currencyValue ?val ; :currency ?cur . ?c :hasInfluencingPerson ?p ; skos:prefLabel ?c_name . ?c2 :hasInfluencingPerson ?p ; skos:prefLabel ?c2_name . ?p skos:prefLabel ?p_name . FILTER (?c != ?c2) FILTER NOT EXISTS { ?p skos:prefLabel ?p_name . FILTER ( regex( lcase(?p_name), "(.*община.*)|(.*държавата.*)|(.*министерст во.*)" )) } } ORDER BY DESC (?val)
  8. 8. http://ec2-34-249-186-171.eu-west- 1.compute.amazonaws.com/sparql Information provided above does not imply that there is any conflict of interest in the examples! It just show-cases that it is possible to view via Linked Open Data people that are influencing both a principle and a Contractor of a government procurement. We do not imply that the companies or the individuals Here have a conflict of interest! Investigative Journalists ought To figure out what is going on.
  9. 9. Visualizations  https://public.tableau.com/profil e/publish/ProcurementBG/Proc urementBG2011- 2016GraphDBEIK#!/publish- confirm  https://public.tableau.com/profil e/publish/Bulstatvs_LocationBG Comapnies/Bulstatvs_Location#! /
  • SamithRadhakrishnan

    Dec. 18, 2018

These are the results of the #datathon17 challenge to create a linked open dataset of Bulgarian trade data.

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