Journal of Data Science

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.

Statistical learning methods have been growing in popularity in recent years. Many of these procedures have parameters that must be tuned for models to perform well. Research has been extensive in neural networks, but not for many other learning methods. We looked at the behavior of tuning parameters for support vector machines, gradient boosting machines, and adaboost in both a classification and regression setting. We used grid search to identify ranges of tuning parameters where good models can be found across many different datasets. We then explored different optimization algorithms to select a model across the tuning parameter space. Models selected by the optimization algorithm were compared to the best models obtained through grid search to select well performing algorithms. This information was used to create an R package, EZtune, that automatically tunes support vector machines and boosted trees.