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

2025-05-14

AI Summary

To address the longstanding issues of rigid tabular storage, semantic impoverishment, and limited analytical capabilities in North Macedonia’s public procurement data, this paper proposes an end-to-end semanticization framework. First, we design and formalize the first domain-specific ontology (in OWL) tailored to the country’s procurement ecosystem. Second, we implement an automated ETL pipeline that transforms structured contract records into a compliant RDF knowledge graph. Third, we integrate SPARQL-based semantic querying with a hybrid XGBoost–LSTM model to enable both trend analysis and risk prediction. This work marks a paradigm shift from static reporting to a reasoning-capable, predictive procurement intelligence system. It significantly enhances data transparency, cross-departmental semantic interoperability, and real-time risk detection. The resulting knowledge infrastructure supports verifiable, evidence-based policymaking and regulatory oversight—demonstrating scalability and reproducibility for similar public-sector data modernization initiatives.

Technology Category

• Data Mining & Knowledge Management: Semantic Web
• Knowledge Representation and Reasoning: Ontologies
• Natural Language Processing: Lexical Semantics and Morphology

Application Category

• Semantics and Knowledge: Scalable techniques for the creation, curation, publication, maintenance, and consumption of large, Web-based, structured, reusable, knowledge graphs and ontologies
• Security and Privacy: Data transparency and provenance
• Economics, Online Markets and Human Computation: Data quality aspects of human-annotated datasets

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.

Problem

• Transforming rigid procurement data into semantic knowledge graphs
• Enhancing accessibility and analysis via RDF and SPARQL querying
• Enabling predictive insights into procurement trends and risks

Innovation

• Transforms procurement data into semantic knowledge graph
• Uses RDF and SPARQL for enhanced querying
• Incorporates machine learning for predictive modeling