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Statistical Relational Learning: Unifying AI & DB Perspectives on Structured Probabilistic Models

Summary: Survey of statistical relational learning bridging ML and probabilistic-database perspectives by contrasting learning/inference vs. uncertain-data storage/querying. Highlights shared model formalisms, key divergences, and recent work on Probabilistic Soft Logic. (summarized by gpt-5-mini on Feb 09 2026)

Paper ID
1738
Venue
PODS
Year
2017
Pagerank
-
Overall Rank
13,530 | 7.18%
DOI
10.1145/3034786.3056450

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BibTeX Citation

@inproceedings{getoor_pods17,
        address = {New York, NY, USA},
        series = {{PODS} '17},
        title = {{Statistical Relational Learning: Unifying AI \& DB Perspectives on Structured Probabilistic Models}},
        url = {https://dl.acm.org/doi/10.1145/3034786.3056450},
        doi = {10.1145/3034786.3056450},
        booktitle = {Proceedings of the {ACM} {SIGMOD} Symposium on {Principles} of {Database} {Systems}},
        publisher = {Association for Computing Machinery},
        author = {Getoor, Lise},
        year = {2017}
}

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