SemanIFC: A Modular Python-Based IFC Parser and RDF Converter for Semantic BIM Validation

This study introduces the open-source SemanIFC software developed to address the need for semantic validation in Building Information Modeling (BIM) processes. The research background reveals that most existing tools rely on pre-compiled ontologies or external engines, making it difficult to adapt to local regulations. The main objective of this study is to develop a lightweight, modular, and script-based system that directly converts Industry Foundation Classes (IFC) files into RDF triples and performs semantic validation using user-defined SPARQL rules. The methodology adopts a Python-based modular architecture. The system consists of independent components such as parser, entity definition, RDF mapper, validator, and exporter. Open-source technologies including RDFLib, SPARQLWrapper, and Flask were integrated to provide flexibility, transparency, and scalability. The web-based interface, which includes file upload, analysis, and data management panels, supports usability and traceability. Application-level tests demonstrated that SemanIFC can ontologically classify building elements and detect semantic inconsistencies. For example, missing definitions and conflicting properties were successfully identified and reported in sample projects. These results validate the effectiveness of the proposed approach in providing transparent and traceable validation. In conclusion, SemanIFC offers a sustainable infrastructure for semantic BIM validation. Its independent structure from external engines and compatibility with open standards make it suitable for both academic research and industrial applications. Furthermore, it provides scalability and long-term contribution to BIM validation workflows by laying the groundwork for future integrations with regulatory compliance modules and linked data platforms.