OntoSage: Intelligent human building smartbot for semantic smart buildings question answering

Devmane, Suhas, Rana, Omer ORCID: https://orcid.org/0000-0003-3597-2646 and Perera, Charith ORCID: https://orcid.org/0000-0002-0190-3346 2026. OntoSage: Intelligent human building smartbot for semantic smart buildings question answering. World Wide Web Item availability restricted.

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Abstract

Smart buildings remain heterogeneous across sensing infrastructure, metadata quality, legacy protocols, and analytics requirements, hindering reusable human–building natural language interfaces. We present OntoSage, a modular framework for ontologically grounded question answering (QA) and fulfillment of analytic intents over smart building data. The framework (i) leverages Brick Schema-based RDF model with reasoning capabilities, (ii) translates natural language (NL) questions into executable SPARQL via a fine-tuned seq2seq model (T5-Base), and (iii) orchestrates portable analytics microservices that operate on time-series sensor data referenced through ontology-linked UUIDs. A summarization component (open-weights Mistral-7B, zero-shot) converts structured SPARQL/SQL/analytic outputs into concise stakeholder-aware responses without requiring task-specific fine-tuning. We categorize QA complexity into four reasoning classes and report component-level execution metrics supporting these categories. To address portability, we formalize a lightweight adaptation workflow (ontology ingestion→entity enrichment for NLU→NL2SPARQL validity checks→analytics binding) designed to minimize per-building retraining. Reproducibility is enabled through public source code, synthetic and ontology-derived datasets, Docker/Compose service descriptors, and documented supporting scripts∗. The developers’ documentation is publicly accessible†.

Item Type:	Article
Status:	In Press
Schools:	Schools > Computer Science & Informatics
Subjects:	Q Science > Q Science (General) Q Science > QA Mathematics > QA76 Computer software T Technology > TH Building construction
Publisher:	Springer Verlag (Germany)
ISSN:	1386-145X
Date of First Compliant Deposit:	8 January 2026
Date of Acceptance:	6 January 2026
Last Modified:	08 Jan 2026 14:15
URI:	https://orca.cardiff.ac.uk/id/eprint/183621

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