OntoSage: Intelligent Human-Building Smartbot for Semantic Smart Building Question Answering
Suhas Devmane, OMER F. RANA, Charith Perera · World Wide Web · 2026
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 )”.