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New benchmark dataset aims to improve natural language to SPARQL querying for building automation

By PulseAugur Editorial · [1 sources] ·

Researchers have introduced Build2SPARQL, a large-scale benchmark dataset designed to improve the translation of natural language questions into SPARQL queries for building automation knowledge graphs. The dataset addresses the scarcity of such benchmarks by generating executable SPARQL queries through a graph-traversal code pipeline, ensuring correctness independently of language model behavior. Natural language questions are then generated by large language models, achieving high semantic fidelity and naturalness in human validation. This benchmark aims to enhance the capabilities of language agents in querying complex building knowledge graphs. AI

IMPACT This benchmark could significantly improve the ability of AI agents to query complex building knowledge graphs, enabling more sophisticated automation and control systems.

RANK_REASON The cluster describes a new benchmark dataset for a specific AI task, presented in an academic paper. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.AI →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

New benchmark dataset aims to improve natural language to SPARQL querying for building automation

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The cluster describes a new benchmark dataset for a specific AI task, presented in an academic paper. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.AI TIER_1 English(EN) · Wooyoung Jung ·

    Build2SPARQL: A Large-Scale Text-to-SPARQL Benchmark Dataset for Building Knowledge Graph Querying

    arXiv:2610.00224v1 Announce Type: new Abstract: Building automation systems are increasingly represented as semantic knowledge graphs (KGs) using ontologies such as Brick and ASHRAE 223P, creating a machine-readable substrate for artificial-intelligence applications. One promisin…

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