On the Parameterized Complexity of Learning First-Order Logic
Summary: Establishes parameterized complexity bounds for learning first-order queries: proves learning is at least as hard as FO model-checking, yielding AW[*]-hardness on general structures. Gives an FPT agnostic PAC algorithm for FO learning over nowhere-dense (sparse) data. (summarized by gpt-5-mini on Feb 09 2026)
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Authors
- 1. Steffen van Bergerem (Rheinisch-Westfaelische Technische Hochschule Aachen)
- 2. Martin Grohe (Rheinisch-Westfaelische Technische Hochschule Aachen)
- 3. Martin Ritzert (Aarhus University)
BibTeX Citation
@inproceedings{bergerem_pods22,
address = {New York, NY, USA},
series = {{PODS} '22},
title = {{On the Parameterized Complexity of Learning First-Order Logic}},
url = {https://dl.acm.org/doi/10.1145/3517804.3524151},
doi = {10.1145/3517804.3524151},
booktitle = {Proceedings of the {ACM} {SIGMOD} Symposium on {Principles} of {Database} {Systems}},
publisher = {Association for Computing Machinery},
author = {van Bergerem, Steffen and Grohe, Martin and Ritzert, Martin},
year = {2022}
}
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Outgoing Citations (Sorted by Pagerank)
Showing 3 of 3 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
| Rank | Cited Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 2,215 | Characterizing Schema Mappings via Data Examples | 2010 | PODS | 8.93781e-05 |
| 2,639 | Learning and Verifying Quantified Boolean Queries by Example | 2013 | PODS | 8.3078378e-05 |
| 5,859 | Active Learning of GAV Schema Mappings | 2018 | PODS | 6.0637959e-05 |
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