Leveraging Survey Metadata for LLM Reasoning via Knowledge Graphs

Statistical survey metadata contains essential contextual information that underpins the accurate interpretation, discovery, and reuse of statistical data. However, traditional metadata formats are not optimized for consumption by large language models (LLMs), which increasingly function as interfaces for data exploration, question-answering, and decision support. This work introduces a knowledge graph-based approach to modeling survey metadata using semantic web standards and linked data principles, specifically designed to make metadata machine-understandable and LLM-compatible. The core metadata entities, including surveys, datasets, variables, concepts, populations, and provenance, are modeled as rich interlinked nodes that allow reasoning, contextual enrichment, and structured prompting. The graph integrates established ontologies such as the Resource Description Framework (RDF) to promote interoperability and alignment with global standards. We demonstrate how this structure allows LLMs to surface relevant metadata, ground their outputs in authoritative sources, and generate semantically precise responses. This approach enhances transparency, facilitates metadata reuse, and supports the development of artificial intelligence (AI) applications powered by statistical products.