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)
Incoming Non-self Citations Over Time
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Incoming Citations (Sorted by Pagerank)
Showing 2 of 2 citing papers.
| Rank | Citing Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 7,868 | Learning Over Dirty Data Without Cleaning | 2020 | SIGMOD | 4.6276013e-05 |
| 9,885 | Scalable and Usable Relational Learning With Automatic Language Bias | 2021 | SIGMOD | 4.2580321e-05 |
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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.
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| 5,787 | Machine Learning for Databases | 2021 | VLDB | 5.3256401e-05 |
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| 9,834 | Is Data Management the Beating Heart of AI Systems? | 2022 | SIGMOD | 4.2706095e-05 |
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| 1,534 | Data Management in Machine Learning: Challenges, Techniques, and Systems | 2017 | SIGMOD | 0.00011462072 |