SignKG: Spatially aware urban business knowledge graph construction from Vietnamese street signboards

Street-level signboards contain valuable urban business information, yet conventional OCR-based systems typically return isolated text strings without identifying their semantic roles or relationships. This limitation is particularly challenging for Vietnamese signboards because of decorative fonts, complex diacritics, irregular layouts, and ambiguous text content. This paper proposes SignKG, a spatially aware framework that transforms Vietnamese street-signboard images into structured urban business knowledge graphs. SignKG integrates visual perception, spatially aware entity extraction, constrained relation inference, and graph optimisation. The framework jointly exploits lexical semantics, textbox positions, neighbourhood context, business rules, and symbolic compatibility constraints to identify business entities and construct semantically consistent relations. Experiments on a real-world Vietnamese signboard dataset show that SignKG achieves entity-, relation-, and graph-level F1 scores of 0.910, 0.874, and 0.851, respectively. It also obtains a valid-edge ratio of 0.967 and a store-connectivity ratio of 0.951, outperforming rule-based, text-only, layout-aware, and LLM-based baselines. Ablation results confirm that spatial information is critical for entity disambiguation, while symbolic constraints improve graph validity and structural consistency.