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The Use of National Strategic Reference Framework Data in Knowledge Graphs and Data Mining to Identify Red Flags

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  • Charalampos Bratsas

    (School of Mathematics, Aristotle University of Thessaloniki, 54124 Thessaloniki, Greece
    Open Knowledge Foundation Greece, 54352 Thessaloniki, Greece)

  • Evangelos Chondrokostas

    (School of Mathematics, Aristotle University of Thessaloniki, 54124 Thessaloniki, Greece
    Open Knowledge Foundation Greece, 54352 Thessaloniki, Greece)

  • Kleanthis Koupidis

    (School of Mathematics, Aristotle University of Thessaloniki, 54124 Thessaloniki, Greece
    Open Knowledge Foundation Greece, 54352 Thessaloniki, Greece)

  • Ioannis Antoniou

    (School of Mathematics, Aristotle University of Thessaloniki, 54124 Thessaloniki, Greece
    Open Knowledge Foundation Greece, 54352 Thessaloniki, Greece)

Abstract

Red Flags in fiscal projects are warning signs that may indicate underlying problems with their implementation. In this paper, we present how National Strategic Reference Framework Open Data can be used to take full advantage of semantic web technologies and data mining techniques to build a knowledge-based system that identifies Red Flags. We collected the data from the Open Data API provided by the Greek Ministry of Economy and Finance. Data modeling consist of two ontologies; the Vocabulary of Fiscal Projects, describing the fiscal projects and the National Strategic Reference Framework Greece Vocabulary, illustrating the Greek National Strategic Reference Framework data. We transformed the data into RDF triples and uploaded them onto an OpenLink Virtuoso Server, so that we could retrieve them via SPARQL queries. Performance indicators were defined to assess the state of the project and Density-Based Spatial Clustering of Applications with Noise, (DBSCAN) was used to identify Red Flags. User’s demands is that rejected projects should raise Red Flags, to avoid project failure and assist the auditor to organize the monitoring process efficiently, by avoiding to examine most of the non-problematic projects. We performed a use case scenario in which an auditor has to examine NSRF projects, approximately 12 months before the end of the programming period. The system retrieved the fiscal information, calculated the performance indicators and identified the Red Flags. The last update of the projects status after the end of the programming period was retrieved and extracted the number of rejected projects, to test whether the user requirements are satisfied. Rejected projects consist of 3.8% of the total projects. The results of the use case scenario show that RedFlags platform is more likely to identify project failures and not raise Red Flags on not rejected projects. Therefore, the RedFlags platform using open data, assists the auditor to organize the monitoring process better.

Suggested Citation

  • Charalampos Bratsas & Evangelos Chondrokostas & Kleanthis Koupidis & Ioannis Antoniou, 2021. "The Use of National Strategic Reference Framework Data in Knowledge Graphs and Data Mining to Identify Red Flags," Data, MDPI, vol. 6(1), pages 1-20, January.
    • Handle: RePEc:gam:jdataj:v:6:y:2021:i:1:p:2-:d:474519
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    References listed on IDEAS

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    1. Charles Kenny & Maria Musatova, 2011. "‘Red Flags of Corruption’ in World Bank Projects: An Analysis of Infrastructure Contracts," Chapters, in: Susan Rose-Ackerman & Tina Søreide (ed.), International Handbook on the Economics of Corruption, Volume Two, chapter 18, Edward Elgar Publishing.
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    4. Fen-May Liou, 2008. "Fraudulent financial reporting detection and business failure prediction models: a comparison," Managerial Auditing Journal, Emerald Group Publishing, vol. 23(7), pages 650-662, July.
    5. Paul Coram & Colin Ferguson & Robyn Moroney, 2008. "Internal audit, alternative internal audit structures and the level of misappropriation of assets fraud," Accounting and Finance, Accounting and Finance Association of Australia and New Zealand, vol. 48(4), pages 543-559, December.
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    Cited by:

    1. Kleanthis Koupidis & Charalampos Bratsas & Christos Vlachokostas, 2022. "OpΕnergy: An Intelligent System for Monitoring EU Energy Strategy Using EU Open Data," Energies, MDPI, vol. 15(21), pages 1-15, November.

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