Semantics-aware Dataset Discovery from Data Lakes with Contextualized Column-based Representation Learning
Summary: Starmie enables table-union discovery with unsupervised contrastive, multi-column contextual encoders that learn semantic column representations from pretrained language models. Filter-and-verify scoring plus HNSW indexing improves retrieval effectiveness and delivers up to 3,000× speedups. (summarized by gpt-5.6-luna on Jul 24 2026)
Incoming Non-self Citations Over Time
Authors
- 1. Grace Fan (Northeastern University)
- 2. Jin Wang (Megagon Labs)
- 3. Yuliang Li (Megagon Labs)
- 4. Dan Zhang (Megagon Labs)
- 5. Renée J. Miller (Northeastern University)
BibTeX Citation
@article{fan_vldb23,
title = {{Semantics-aware Dataset Discovery from Data Lakes with Contextualized Column-based Representation Learning}},
author = {Fan, Grace and Wang, Jin and Li, Yuliang and Zhang, Dan and Miller, Renée J.},
journal = {PVLDB},
series = {{VLDB} '23},
volume = {16},
number = {7},
pages = {1726--1739},
doi = {10.14778/3587136.3587146},
url = {https://doi.org/10.14778/3587136.3587146},
year = {2023}
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