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New framework uses LLMs for natural language access to domain-specific archives

By PulseAugur Editorial · [1 sources] ·

Researchers have developed a reusable framework called Natural Language Knowledge Graph Query (NLKGQ) that enables users to query domain-specific archives using natural language. The system leverages Large Language Models (LLMs) to translate natural language questions into accurate SPARQL queries, which are then executed against a knowledge graph. This framework is designed to be domain-agnostic, with development beginning by capturing domain vocabulary and semantics in a Web Ontology Language (OWL) ontology. The system has demonstrated 100% accuracy in querying neuroimaging research archive metadata, with readable entity names and semantic annotations proving more critical than LLM choice or prompt engineering. AI

IMPACT Enables researchers to access domain-specific data more easily, potentially accelerating scientific discovery.

RANK_REASON Academic paper detailing a new framework for LLM-driven query generation. [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 framework uses LLMs for natural language access to domain-specific archives

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Academic paper detailing a new framework for LLM-driven query generation. [lever_c_demoted from research: ic=1 ai=1.0]
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  1. arXiv cs.AI TIER_1 English(EN) · Blake G. Fitch, Cato Elia Kurtz ·

    Natural Language Access to Domain-Specific Metadata: A Reusable Framework for LLM Query Generation

    arXiv:2607.18029v1 Announce Type: cross Abstract: Researchers need to answer ad-hoc questions about the contents of domain-specific archives but often lack the expertise to write structured queries on the metadata. We show that when domain vocabulary and semantics are captured in…

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