Toward a Theory of Multiformat Semantic Embedding for Smart Cities: A Systematic Literature Review on Joint Text and Infographic Representations in Big Data Governance

Resumen

Smart city ecosystems generate diverse data streams, including legal documents, textual citizen reports, sensor dashboards, geospatial maps, and infographics. Despite advances in natural language processing (NLP) embedding, vision-language models, and graph-based urban analytics, a coherent theoretical framework explaining the integration of semantic representations across multiple modalities into a single embedding space for decision-making is lacking. This paper describes a systematic literature review (SLR) pertaining to multi-format semantic embedding in smart cities, emphasizing the combined representation of textual documents and visual artifacts such as infographics (maps, dashboards, plots, and schematic diagrams). Following PRISMA 2020 guidelines, we conducted searches in SpringerLink, Taylor & Francis, and SCOPUS between 2021 and 2025, resulting in 2,662 records and the inclusion of 110 primary studies through a screening process. This state of the art research is based on eight questions addressing data modalities, urban application domains, embedding models, integration mechanisms, evaluation metrics, and governance considerations. The review explains that most studies continue to model each modality separately, cross-modal integration is largely characterized by fusion-based methodologies and knowledge graphs, and evaluations emphasize retrieval and prediction accuracy, with insufficient focus on semantic coherence and interpretability at the policy level. Given these identified gaps, we propose the Theory of Multiformat Semantic Integration (TISM) and the Urban Multiformat Embedding Framework (UMEF), alongside a conceptual metric called Urban Semantic Coherence (USC) to evaluate cross-modal alignment. The review concludes with a proposed research agenda aimed at fostering robust, explainable, and governable multiformat embedding in the context of smart city big data environments.