OntoSage: Intelligent Human-Building Smartbot for Semantic Smart Building Question Answering
Year: 2026
Type: article
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 $$\rightarrow $$ entity enrichment for NLU $$\rightarrow $$ NL2SPARQL validity checks $$\rightarrow $$ 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 “( https://github.com/suhasdevmane/OntoBot )”. The developers’ documentation is publicly accessible “( https://ontosage-docs.github.io )”.
Source: World Wide Web
Authors: Suhas Devmane, Omer Rana, Charith Perera
Institution: Cardiff University
Language: English
Topic: Semantic Web and Ontologies
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
Estimated APC paid: $3,290
Award: PETRAS 2
OpenAlex ID: W7124985587