Collective Grounding: Applying Database Techniques to Grounding Templated Models
Summary: Collective grounding: treat grounding of templated relational models as a joint, interdependent workload to enable shared computation using DB techniques (query planning, join/provenance) to cut grounding cost. Implements components and shows up to 70% runtime reduction on seven datasets; useful for relational learning and non-independent probabilistic DBs. (summarized by gpt-5-mini on Feb 09 2026)
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Authors
- 1. Eriq Augustine
- 2. Lise Getoor
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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 |
|---|---|---|---|---|
| 401 | Conjunctive-Query Containment and Constraint Satisfaction | 1998 | PODS | 0.00024281448 |
| 1,015 | Tuffy: Scaling up Statistical Inference in Markov Logic Networks using an RDBMS | 2011 | VLDB | 0.00014630577 |
| 1,520 | The Containment Problem for Real Conjunctive Queries with Inequalities | 2006 | PODS | 0.00011524911 |
| 3,782 | Bag Query Containment and Information Theory | 2020 | PODS | 6.7691169e-05 |
| 4,162 | SlimShot: In-Database Probabilistic Inference for Knowledge Bases | 2016 | VLDB | 6.386842e-05 |
| 7,797 | ForBackBench: A Benchmark for Chasing vs. Query-Rewriting | 2022 | VLDB | 4.6438053e-05 |
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