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Collective Grounding: Applying Database Techniques to Grounding Templated Models

Summary: Introduces collective grounding, jointly optimizing interdependent template-instantiation workloads rather than grounding rules independently. It adapts relational database techniques to relational learning/probabilistic databases, achieving up to 70% runtime reduction across seven datasets. (summarized by gpt-5.6-luna on Jul 24 2026)

Paper ID
13229
Venue
VLDB
Year
2023
Pagerank
5.093636e-05
Overall Rank
11,432 | 21.57%
DOI
10.14778/3594512.3594516

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BibTeX Citation

@article{augustine_vldb23,
        title = {{Collective Grounding: Applying Database Techniques to Grounding Templated Models}},
        author = {Augustine, Eriq and Getoor, Lise},
        journal = {PVLDB},
        series = {{VLDB} '23},
        volume = {16},
        number = {8},
        pages = {1843--1855},
        doi = {10.14778/3594512.3594516},
        url = {https://doi.org/10.14778/3594512.3594516},
        year = {2023}
}

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