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<p>Smart buildings remain heterogeneous across sensing infrastructure, metadata quality, legacy protocols, and analytics requirements, hindering reusable human–building natural language interfaces. We present <b>OntoSage</b>, 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<InlineEquation ID="IEq1"> <EquationSource Format="TEX">\(\rightarrow \)</EquationSource> </InlineEquation>entity enrichment for NLU<InlineEquation ID="IEq2"> <EquationSource Format="TEX">\(\rightarrow \)</EquationSource> </InlineEquation>NL2SPARQL validity checks<InlineEquation ID="IEq3"> <EquationSource Format="TEX">\(\rightarrow \)</EquationSource> </InlineEquation>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 “(<a href="https://github.com/suhasdevmane/OntoBot">https://github.com/suhasdevmane/OntoBot</a>)”. The developers’ documentation is publicly accessible “(<a href="https://ontosage-docs.github.io">https://ontosage-docs.github.io</a>)”.</p>

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OntoSage: Intelligent Human-Building Smartbot for Semantic Smart Building Question Answering

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年1月20日

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  • Suhas Devmane,
  • Omer Rana,
  • Charith Perera

摘要

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)”.