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Dataset discovery from data lakes is essential in many real applicationscenarios. In this paper, we propose Starmie, an end-to-end framework fordataset discovery from data lakes (with table union search as the main usecase). Our proposed framework features a contrastive learning method to traincolumn encoders from pre-trained language models in a fully unsupervisedmanner. The column encoder of Starmie captures the rich contextual semanticinformation within tables by leveraging a contrastive multi-column pre-trainingstrategy. We utilize the cosine similarity between column embedding vectors asthe column unionability score and propose a filter-and-verification frameworkthat allows exploring a variety of design choices to compute the unionabilityscore between two tables accordingly. Empirical evaluation results on realtable benchmark datasets show that Starmie outperforms the best-known solutionsin the effectiveness of table union search by 6.8 in MAP and recall. Moreover,Starmie is the first to employ the HNSW (Hierarchical Navigable Small World)index for accelerate query processing of table union search which provides a3,000X performance gain over the linear scan baseline and a 400X performancegain over an LSH index (the state-of-the-art solution for data lake indexing).

Semantics-aware Dataset Discovery from Data Lakes with Contextualized Column-based Representation Learning