Cardinality Estimation over Knowledge Graphs with Embeddings and Graph Neural Networks
Summary: GNCE uses knowledge-graph embeddings and Graph Neural Networks to estimate conjunctive-query cardinalities. It outperforms sampling, summaries, and ML baselines, with inductive generalization to unseen entities and a compact parameter footprint. (summarized by gpt-5-nano on Feb 09 2026)
Incoming Non-self Citations Over Time
Authors
- 1. Tim Schwabe
- 2. Maribel Acosta
Incoming Citations (Sorted by Pagerank)
Showing 7 of 7 citing papers.
| Rank | Citing Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 9,728 | SPACE: Cardinality Estimation for Path Queries Using Cardinality-Aware Sequence-based Learning | 2025 | SIGMOD | 4.2942813e-05 |
| 9,845 | Path-centric Cardinality Estimation for Subgraph Matching | 2025 | VLDB | 4.2721228e-05 |
| 10,083 | GeoKGM: A Multimodal Large Language Model for Zero-Shot Knowledge Graph Completion in Geospatial Databases | 2026 | SIGMOD | 4.1945683e-05 |
| 10,096 | NeuSO: Neural Optimizer for Subgraph Queries | 2026 | SIGMOD | 4.1945683e-05 |
| 10,163 | Enumerating Graph Pattern Matches with ML Oracles | 2026 | SIGMOD | 4.1945683e-05 |
| 10,236 | A Semantics-aware Approach for Graph Edit Distance Estimation over Knowledge Graphs | 2026 | VLDB | 4.1945683e-05 |
| 10,564 | PlanRGCN: Predicting SPARQL Query Performance | 2025 | VLDB | 4.1945683e-05 |
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Outgoing Citations (Sorted by Pagerank)
Showing 13 of 13 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
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