Data Quality Barriers for Transparency in Public Procurement

被引:15
作者
Soylu, Ahmet [1 ]
Corcho, Oscar [2 ]
Elvesaeter, Brian [3 ]
Badenes-Olmedo, Carlos [2 ]
Yedro-Martinez, Francisco [2 ]
Kovacic, Matej [4 ]
Posinkovic, Matej [4 ]
Medvescek, Mitja [5 ]
Makgill, Ian [6 ]
Taggart, Chris [7 ]
Simperl, Elena [8 ]
Lech, Till C. [3 ]
Roman, Dumitru [3 ]
机构
[1] OsloMet Oslo Metropolitan Univ, Dept Comp Sci, N-0166 Oslo, Norway
[2] Univ Politecn Madrid, Dept Artificial Intelligence, Madrid 28040, Spain
[3] SINTEF AS, Software & Serv Innovat, N-0373 Oslo, Norway
[4] Jozef Stefan Inst, Ctr Knowledge Transfer Informat Technol, Ljubljana 1000, Slovenia
[5] Govt Slovenia, Minist Publ Adm, Ljubljana 1000, Slovenia
[6] OpenOpps Ltd, London SW1P 2PD, England
[7] OpenCorp Ltd, London N3 1LF, England
[8] Kings Coll London, Dept Informat, London WC2R 2LS, England
基金
欧盟地平线“2020”;
关键词
public procurement; fraud and corruption; data integration; knowledge graph; linked open data; anomaly detection; ONTOLOGY;
D O I
10.3390/info13020099
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
Governments need to be accountable and transparent for their public spending decisions in order to prevent losses through fraud and corruption as well as to build healthy and sustainable economies. Open data act as a major instrument in this respect by enabling public administrations, service providers, data journalists, transparency activists, and regular citizens to identify fraud or uncompetitive markets through connecting related, heterogeneous, and originally unconnected data sources. To this end, in this article, we present our experience in the case of Slovenia, where we successfully applied a number of anomaly detection techniques over a set of open disparate data sets integrated into a Knowledge Graph, including procurement, company, and spending data, through a linked data-based platform called TheyBuyForYou. We then report a set of guidelines for publishing high quality procurement data for better procurement analytics, since our experience has shown us that there are significant shortcomings in the quality of data being published. This article contributes to enhanced policy making by guiding public administrations at local, regional, and national levels on how to improve the way they publish and use procurement-related data; developing technologies and solutions that buyers in the public and private sectors can use and adapt to become more transparent, make markets more competitive, and reduce waste and fraud; and providing a Knowledge Graph, which is a data resource that is designed to facilitate integration across multiple data silos by showing how it adds context and domain knowledge to machine-learning-based procurement analytics.
引用
收藏
页数:21
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