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

Charalampos Bratsas, Evangelos Chondrokostas, Kleanthis Koupidis and Ioannis Antoniou
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Charalampos Bratsas: School of Mathematics, Aristotle University of Thessaloniki, 54124 Thessaloniki, Greece
Evangelos Chondrokostas: School of Mathematics, Aristotle University of Thessaloniki, 54124 Thessaloniki, Greece
Kleanthis Koupidis: School of Mathematics, Aristotle University of Thessaloniki, 54124 Thessaloniki, Greece
Ioannis Antoniou: School of Mathematics, Aristotle University of Thessaloniki, 54124 Thessaloniki, Greece

Data, 2021, vol. 6, issue 1, 1-20

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.

Keywords: Red Flags; Knowledge Graphs; Density Based Clustering; DBSCAN; NSRF Open Data; warning system (search for similar items in EconPapers)
JEL-codes: C8 C80 C81 C82 C83 (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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