Papers by Asterios Katsifodimos
Automatic Table Union Search with Tabular Representation Learning (2023.findings-acl)
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Xuming Hu, Shen Wang, Xiao Qin, Chuan Lei, Zhengyuan Shen, Christos Faloutsos, Asterios Katsifodimos, George Karypis, Lijie Wen, Philip S. Yu
| Challenge: | Existing methods to identify uniability based on column representations are insufficient to reveal latent relational features to describe column relation between pair of columns. |
| Approach: | They propose a self-supervised table union search framework called AutoTUS to learn column relational representations in a multi-stage manner. |
| Outcome: | The proposed framework improves on the SOTA baseline and on real-world datasets. |
PolyJoin: Semantic Multi-key Joinable Table Search in Data Lakes (2025.findings-naacl)
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| Challenge: | Existing joinable table search methods focus on single key (unary) joins, where a single column is the join key, but are ineffective when dealing with join keys composed of multiple columns (n-ary joins) Existing methods are inefficient when dealing . with joins composed of n-aries, which are prevalent on web table corpora. |
| Approach: | They propose a joinable table search method that finds multi-key joinable tables on the web, given a query table. |
| Outcome: | The proposed method outperforms the state-of-the-art methods on two real-world web table benchmarks. |
DiscoverGPT: Multi-task Fine-tuning Large Language Model for Related Table Discovery (2025.findings-naacl)
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Xuming Hu, Xiao Qin, Chuan Lei, Asterios Katsifodimos, Zhengyuan Shen, Balasubramaniam Srinivasan, Huzefa Rangwala
| Challenge: | Existing methods to learn and evaluate the table semantic relatedness of tabular data are based on pretrain-and-finetune paradigms. |
| Approach: | They propose a multi-task fine-tuning framework that holistically discovers and leverages the intricate relationships among the supervisions to optimize the performance on the data discovery task. |
| Outcome: | The proposed framework outperforms the best performing baseline by up to 7% in F1 score. |