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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)

Paper ID
1877
Venue
PODS
Year
2022
Pagerank
5.093636e-05
Overall Rank
11,522 | 20.95%
DOI
10.1145/3517804.3524151

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Authors

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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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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