用知识图谱让北马其顿采购数据可查可分析
Ontology-Based Structuring and Analysis of North Macedonian Public Procurement Contracts
- 用本体建模把表格数据转为语义图谱
- 支持复杂查询与采购趋势预测分析
- 适合政府透明化与政策研究者使用
公共采购在政府运作中至关重要，能有效配置资源并促进经济增长。然而，传统采购数据多以固定表格形式存储，限制了分析潜力并阻碍透明度。本文提出一种方法论框架，将结构化采购数据转化为基于语义的知识图谱，利用本体建模与自动化数据转换技术，结合RDF与SPARQL查询能力，提升采购记录的可访问性与可解释性，支持复杂语义查询与高级分析。进一步通过机器学习驱动的预测建模，超越传统数据分析，提供采购趋势洞察与风险评估。该研究推动了公共采购智能领域发展，提升了数据透明度，支持证据决策，并实现对北马其顿采购活动的深入分析。
原文摘要 · Abstract (English)
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.
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