Creating Embeddings of Heterogeneous Relational Datasets for Data Integration Tasks
Summary: Proposes EmbDI, local relational embeddings learned from a compact graph-based representation for data integration. Derives sentences from the graph to express similarity across tokens, attributes, and rows, aiding schema matching and entity resolution. (summarized by gpt-5-nano on Feb 09 2026)
Incoming Non-self Citations Over Time
Authors
- 1. Riccardo Cappuzzo (EURECOM)
- 2. Paolo Papotti (EURECOM)
- 3. Saravanan Thirumuruganathan (Hamad Bin Khalifa University; Qatar Computing Research Institute)
BibTeX Citation
@inproceedings{cappuzzo_sigmod20,
title = {{Creating Embeddings of Heterogeneous Relational Datasets for Data Integration Tasks}},
author = {Cappuzzo, Riccardo and Papotti, Paolo and Thirumuruganathan, Saravanan},
series = {{SIGMOD} '20},
booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
publisher = {Association for Computing Machinery},
doi = {10.1145/3318464.3389742},
url = {https://dl.acm.org/doi/10.1145/3318464.3389742},
year = {2020}
}
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