Abstract
High-speed railway catenary maintenance increasingly requires knowledge bases that can connect maintenance records with geometric infrastructure models for reliable digital twin-enabled decision support. However, existing knowledge graph construction methods in engineering settings often struggle with severe label sparsity, weak instance-level grounding, and limited physical interpretability. To address these issues, we propose PhyGeo-KG, a physics-regularized distant supervision framework for constructing high-fidelity multimodal geometric knowledge graphs for catenary maintenance. The framework consists of three main phases: (i) a Semantic-Geometric-Physical-Procedural ontology for unifying heterogeneous engineering information; (ii) a deterministic grounding strategy that aligns textual mentions with Industry Foundation Classes (IFC)/Building Information Modeling (BIM) entities through geometric interfaces; and (iii) a physics-aware refinement and ontology-driven evolution process that removes physically implausible relations while expanding the validated graph. Experiments on a real-world dataset constructed from IFC-compliant BIM models of a 2 km high-speed railway catenary section and associated maintenance documents show that the proposed approach improves relation precision and physical consistency while effectively suppressing semantic hallucinations. A case study further demonstrates its potential for instance-level fault localization in semantic digital twins. These results indicate that PhyGeo-KG provides an interpretable and transferable foundation for physics-regularized multimodal geometric knowledge graph construction and digital-twin-enabled decision support in catenary maintenance, with potential to support future sensor-integrated maintenance applications.