Summary of Improving Rule Mining Via Embedding-based Link Prediction, by N’dah Jean Kouagou et al.
Improving rule mining via embedding-based link prediction
by N’Dah Jean Kouagou, Arif Yilmaz, Michel Dumontier, Axel-Cyrille Ngonga Ngomo
First submitted to arxiv on: 14 Jun 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: None
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed hybrid approach combines rule mining and embedding-based methods for link prediction on knowledge graphs. The existing methods are either interpretable but lack generalization capabilities or generalize well but provide uninterpretable predictions. To overcome this limitation, the authors propose enriching a given knowledge graph with pre-trained entity and relation embeddings before applying rule mining systems. This approach is validated through extensive experiments on seven benchmark datasets, showing the discovery of new valuable rules on the enriched graphs. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper combines two different approaches to link prediction on knowledge graphs: rule mining for explainable results and embedding-based methods for generalization capabilities. To make them work together, the authors enrich a graph with pre-trained entity and relation embeddings before applying rule mining systems. This helps discover new valuable rules on the enriched graphs. The approach is tested on seven benchmark datasets. |
Keywords
» Artificial intelligence » Embedding » Generalization » Knowledge graph
