Burr: A Benchmark for Ontology Learning from Relational Databases
Summary: Burr: a benchmark and mapping-based evaluation metric for ontology learning from relational databases, with 54 scenarios of real-world DB–ontology mappings (including industry datasets) and a micro-benchmark. Evaluation shows rule-based methods currently beat LLMs, but LLMs hold significant promise. (summarized by gpt-5-mini on Feb 11 2026)
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Authors
- 1. Lukas Laskowski (Hasso Plattner Institute; University of Potsdam)
- 2. Michael Hladik (SAP)
- 3. Jan Portisch (SAP)
- 4. Fabian Panse (University of Augsburg)
- 5. Felix Naumann (Hasso Plattner Institute; University of Potsdam)
BibTeX Citation
@inproceedings{laskowski_sigmod26,
title = {{Burr: A Benchmark for Ontology Learning from Relational Databases}},
author = {Laskowski, Lukas and Hladik, Michael and Portisch, Jan and Panse, Fabian and Naumann, Felix},
series = {{SIGMOD} '26},
booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
publisher = {Association for Computing Machinery},
doi = {10.1145/3769770},
url = {https://dl.acm.org/doi/10.1145/3769770},
year = {2026}
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| 397 | TURL: Table Understanding through Representation Learning | 2021 | VLDB | 0.00019278189 |
| 8,844 | Hitting Set Enumeration with Partial Information for Unique Column Combination Discovery | 2020 | VLDB | 5.3588479e-05 |
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