Schema Independent and Scalable Relational Learning By Castor
Summary: Castor achieves schema-independent relational learning, producing the same target definitions across common schema variations. It is scalable to large data and delivers higher accuracy than well-known learning systems over big datasets. (summarized by gpt-5-nano on Feb 09 2026)
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Authors
- 1. Jose Picado (Oregon State University)
- 2. Parisa Ataei (Oregon State University)
- 3. Arash Termehchy (Oregon State University)
- 4. Alan Fern (Oregon State University)
BibTeX Citation
@article{picado_vldb16,
title = {{Schema Independent and Scalable Relational Learning By Castor}},
author = {Picado, Jose and Ataei, Parisa and Termehchy, Arash and Fern, Alan},
journal = {PVLDB},
series = {{VLDB} '16},
volume = {9},
number = {13},
pages = {1589--1592},
doi = {10.14778/3007263.3007269},
url = {https://doi.org/10.14778/3007263.3007269},
year = {2016}
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Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
| Rank | Cited Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 4,190 | QuickFOIL: Scalable Inductive Logic Programming | 2015 | VLDB | 6.8431849e-05 |
| 7,985 | Schema Independent Relational Learning | 2017 | SIGMOD | 5.5125969e-05 |
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