Hamilton– Interactive Ontology Learning
Summary: Hamilton interactively learns ontologies from relational schemas by combining automated modeling suggestions with expert refinement. Its distinguishing feature is a human-in-the-loop workflow addressing the semantic gap between integrity-oriented database design and context-dependent ontology modeling. (summarized by gpt-5.6-luna on Aug 28 2026)
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Authors
- 1. Lukas Laskowski (Hasso Plattner Institute; University of Potsdam)
- 2. Felix Draxler (University of California Irvine)
- 3. Michael Hladik (SAP)
- 4. Fabian Panse (University of Augsburg)
- 5. Jan Portisch (SAP)
- 6. Padhraic Smyth (University of California Irvine)
- 7. Felix Naumann (Hasso Plattner Institute; University of Potsdam)
BibTeX Citation
@article{laskowski_vldb26,
title = {{Hamilton– Interactive Ontology Learning}},
author = {Laskowski, Lukas and Draxler, Felix and Hladik, Michael and Panse, Fabian and Portisch, Jan and Smyth, Padhraic and Naumann, Felix},
journal = {PVLDB},
series = {{VLDB} '26},
volume = {19},
number = {12},
pages = {4534--4537},
doi = {10.14778/3827998.3828059},
url = {https://doi.org/10.14778/3827998.3828059},
year = {2026}
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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 |
|---|---|---|---|---|
| 11,050 | Schuyler: Self-Supervised Clustering of Tables in Relational Databases | 2026 | VLDB | 4.9793485e-05 |
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