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ORIGINAL RESEARCH article

Front. Built Environ.

Sec. Urban Science

Volume 11 - 2025 | doi: 10.3389/fbuil.2025.1674307

This article is part of the Research TopicExtended Mind for the Design of Human EnvironmentView all 17 articles

A BEACON through the Walls: AI-assisted Tacit Knowledge Extraction from Built Environments

Provisionally accepted
  • University of Bologna, Bologna, Italy

The final, formatted version of the article will be published soon.

The rapid urbanization of contemporary society has created environments that often overlook the human needs of their inhabitants. This paper presents BEACON (Built Environment Architecture Cognitive Ontology Network), a comprehensive multi-layer ontological framework to support reasoning about the gaps between practical urban design and the requirements that emerge from social, cognitive and neuroarchitectural findings concerning urban living. BEACON integrates seven analytical layers—physical, experiential, social, normative, behavioral, cognitive, and neural—a systematic network with descriptions ranging from physical design elements to individual neural responses. Integrating those layers addresses critical limitations in current neuroarchitecture research by providing: (1) a formal ontological structure for organizing complex environmental-neural relationships, (2) a practical methodology for extracting tacit knowledge from built environments, applying it to an analysis of Pachino's central square in Sicily, comparing historical (1910) and contemporary (2025) configurations to reveal how architectural modifications cascade through all analytical dimensions, and (3) an example design of an immersive XR platform for both research and applied urban planning, enabling real-time, multi-sensory analysis of urban environments. This transdisciplinary integration envisages a paradigm shift from post-hoc environmental analysis to proactive design optimization.

Keywords: Neuroarchitecture, Neurosymbolic AI, Ontology design, Urban cognition, Extended Reality, built environment, Neural Response

Received: 27 Jul 2025; Accepted: 20 Oct 2025.

Copyright: © 2025 Gangemi and Lucifora. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

* Correspondence:
Aldo Gangemi, aldo.gangemi@unibo.it
Chiara Lucifora, chiara.lucifora@unibo.it

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