Paying down metadata debt: learning the representation of concepts using topic models
This addresses metadata management issues for data scientists and researchers, though it is incremental as it builds on existing topic modeling techniques.
The paper tackles the problem of metadata debt by learning mappings between data concepts and their representations using a semisupervised topic model, achieving results in predicting subject tags on over 25,000 Kaggle datasets.
We introduce a data management problem called metadata debt, to identify the mapping between data concepts and their logical representations. We describe how this mapping can be learned using semisupervised topic models based on low-rank matrix factorizations that account for missing and noisy labels, coupled with sparsity penalties to improve localization and interpretability. We introduce a gauge transformation approach that allows us to construct explicit associations between topics and concept labels, and thus assign meaning to topics. We also show how to use this topic model for semisupervised learning tasks like extrapolating from known labels, evaluating possible errors in existing labels, and predicting missing features. We show results from this topic model in predicting subject tags on over 25,000 datasets from Kaggle.com, demonstrating the ability to learn semantically meaningful features.