SemanDICT: A Python-Based Semantic Dictionary Engine for IFC Objects with RDF Generation and SPARQL Querying

Year 2025, Volume: 12 Issue: 4, 1088 - 1120, 31.12.2025

Murat Aydın

https://doi.org/10.54287/gujsa.1800713

Abstract

This study presents SemanDICT, a modular semantic dictionary system designed to enhance the semantic richness, traceability, and interoperability of IFC-based Building Information Modeling (BIM) data. The system supports RDF triple generation, SPARQL querying, and multi-user data editing processes through interface panels structured according to ontology engineering principles. Users can select IFC classes to define data types and unit-defined properties; semantic consistency can be verified through bSDD integration. The AI Feature Assistant Panel suggests missing features based on contextual inference, while the Material Properties Database supports domain-focused semantic modeling. RDF export is performed in OWL-compliant formats such as Turtle, RDF/XML, and JSON-LD. The panel-based workflow covers eight core processes: IFC class selection, property suggestion, semantic definition, ontological validation, RDF export, SPARQL querying, statistical analysis, and project collaboration. Each process is paired with a corresponding interface panel, offering modular interaction and extensible development capabilities. The SPARQL Query Panel facilitates semantic queries, while the Dictionary Statistics Panel visualizes data type distribution and semantic density. The Project Collaboration Panel supports multi-user development with simultaneous editing and version control. SemanDICT contributes to academia and industry in the areas of ontology-focused design, data management, and open standards by bringing semantic web technologies together with BIM production environments.

Keywords

Semantic Dictionary , BIM (Building Information Modeling) , IFC (Industry Foundation Classes) , RDF (Resource Description Framework) , SPARQL (SPARQL Protocol and RDF Query Language) , Ontology , Python

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