Ontology-Based Structuring and Analysis of North Macedonian Public Procurement Contracts
Bojan Ristov, Stefan Eftimov, Milena Trajanoska, Dimitar Trajanov
TL;DR
This work tackles the opacity of North Macedonian public procurement data by introducing an ontology-driven framework that converts tabular records into a semantic RDF knowledge graph. It combines CSV-to-RDF transformation via RML, SHACL-based validation, and SPARQL analytics to enable structured querying and Linked Open Data interoperability. A machine learning component using multilingual-e5-large-instruct embeddings and a FAISS index provides predictive estimation of contract values and historical trend visualizations, facilitating data-driven procurement planning. Overall, the approach enhances transparency, supports evidence-based decision-making, and enables in-depth procurement analysis for policymakers and researchers.
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.