Ontology-Based Structuring and Analysis of North Macedonian Public Procurement Contracts

TL;DR

This paper introduces an ontological framework that transforms North Macedonian public procurement data into a semantic knowledge graph, enhancing transparency, analysis, and predictive insights for better decision-making.

Contribution

It presents a novel methodology combining ontological modeling, semantic querying, and machine learning to improve procurement data analysis and transparency.

Findings

Enhanced data accessibility through semantic knowledge graphs

Improved procurement trend analysis and risk assessment

Supports evidence-based decision-making in public procurement

Abstract

Public procurement plays a critical role in government operations, ensuring the efficient allocation of resources and fostering economic growth. However, traditional procurement data is often stored in rigid, tabular formats, limiting its analytical potential and hindering transparency. This research presents a methodological framework for transforming structured procurement data into a semantic knowledge graph, leveraging ontological modeling and automated data transformation techniques. By integrating RDF and SPARQL-based querying, the system enhances the accessibility and interpretability of procurement records, enabling complex semantic queries and advanced analytics. Furthermore, by incorporating machine learning-driven predictive modeling, the system extends beyond conventional data analysis, offering insights into procurement trends and risk assessment. This work contributes to the broader field of public procurement intelligence by improving data transparency, supporting evidence-based decision-making, and enabling in-depth analysis of procurement activities in North Macedonia.