Machine Learning for Data Management: Problems and Solutions

Summary: Tractable Markov logic for data management models non-i.i.d., multi-relational data with convex parameter learning and ILP-based structure learning. Inference via probabilistic theorem proving enables exact, subsecond queries on an RDBMS-backed knowledge base, with applications to entity resolution, schema matching, ontology alignment, and information extraction. (summarized by gpt-5-nano on Feb 09 2026)

Paper ID: 5652
Venue: SIGMOD
Year: 2018
Pagerank: 5.3867577e-05
Overall Rank: 8,676 | 40.48%
DOI: 10.1145/3183713.3199515

Incoming Non-self Citations Over Time

Authors

1. Pedro Domingos (University of Washington)

BibTeX Citation

@inproceedings{domingos_sigmod18,
        title = {{Machine Learning for Data Management: Problems and Solutions}},
        author = {Domingos, Pedro},
        series = {{SIGMOD} '18},
        booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
        publisher = {Association for Computing Machinery},
        doi = {10.1145/3183713.3199515},
        url = {https://dl.acm.org/doi/10.1145/3183713.3199515},
        year = {2018}
}

Incoming Citations (Sorted by Pagerank)

Showing 2 of 2 citing papers.

Rank	Citing Paper	Year	Venue	Pagerank
7,880	Learning Over Dirty Data Without Cleaning	2020	SIGMOD	5.5244204e-05
10,042	Scalable and Usable Relational Learning With Automatic Language Bias	2021	SIGMOD	5.1708123e-05

Outgoing Citations (Sorted by Pagerank)

Showing 0 of 0 cited papers.

Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.

Rank	Cited Paper	Year	Venue	Pagerank

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