eXpath: Explaining Knowledge Graph Link Prediction with Ontological Closed Path Rules
Summary: eXpath leverages ontological closed-path rules to produce semantically meaningful path-based explanations for embedding-based KG link prediction, moving beyond local single-link justifications. Improves explanation quality ≈20%, reduces explanation time 61.4%, and composes with single-link explainers. (summarized by gpt-5-mini on Feb 09 2026)
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Authors
- 1. Ye Sun
- 2. Lei Shi
- 3. Yongxin Tong
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Showing 6 of 6 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 | 0.0006429466 |
| 804 | YAGO3: A Knowledge Base from Multilingual Wikipedias | 2015 | CIDR | 0.00016463579 |
| 4,205 | Association Rules with Graph Patterns | 2015 | VLDB | 6.3597474e-05 |
| 6,408 | Explaining Link Prediction Systems based on Knowledge Graph Embeddings | 2022 | SIGMOD | 5.0763482e-05 |
| 7,287 | Discovering Association Rules from Big Graphs | 2022 | VLDB | 4.7762276e-05 |
| 7,355 | Kelpie: an Explainability Framework for Embedding-based Link Prediction Models | 2022 | VLDB | 4.7529612e-05 |
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