Explaining Link Prediction Systems based on Knowledge Graph Embeddings
Summary: Introduces Kelpie, a model-agnostic explainability framework for embedding-based link prediction in knowledge graphs. It extracts necessary and sufficient explanations by tracing training facts, enabling interpretable predictions across LP models and outperforming baselines in experiments. (summarized by gpt-5-nano on Feb 09 2026)
Incoming Non-self Citations Over Time
Authors
- 1. Andrea Rossi
- 2. Donatella Firmani
- 3. Paolo Merialdo
- 4. Tommaso Teofili
Incoming Citations (Sorted by Pagerank)
Showing 4 of 4 citing papers.
| Rank | Citing Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 5,570 | Accelerating Sampling and Aggregation Operations in GNN Frameworks with GPU Initiated Direct Storage Accesses | 2024 | VLDB | 5.4280174e-05 |
| 7,352 | Kelpie: an Explainability Framework for Embedding-based Link Prediction Models | 2022 | VLDB | 4.7484027e-05 |
| 10,648 | eXpath: Explaining Knowledge Graph Link Prediction with Ontological Closed Path Rules | 2025 | VLDB | 4.1905499e-05 |
| 10,889 | Efficient Graph Embedding Generation and Update for Large-Scale Temporal Graph | 2025 | VLDB | 4.1905499e-05 |
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Outgoing Citations (Sorted by Pagerank)
Showing 5 of 5 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
| Rank | Cited Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 62 | Freebase: A Collaboratively Created Graph Database For Structuring Human Knowledge | 2008 | SIGMOD | 0.00064239035 |
| 219 | Deep Entity Matching with Pre-Trained Language Models | 2021 | VLDB | 0.00033354456 |
| 293 | Deep Learning for Entity Matching: A Design Space Exploration | 2018 | SIGMOD | 0.00028661817 |
| 805 | YAGO3: A Knowledge Base from Multilingual Wikipedias | 2015 | CIDR | 0.00016431373 |
| 1,969 | Realistic Re-evaluation of Knowledge Graph Completion Methods: An Experimental Study | 2020 | SIGMOD | 9.9079644e-05 |
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