Summary of A*net and Nbfnet Learn Negative Patterns on Knowledge Graphs, by Patrick Betz et al.
A*Net and NBFNet Learn Negative Patterns on Knowledge Graphs
by Patrick Betz, Nathanael Stelzner, Christian Meilicke, Heiner Stuckenschmidt, Christian Bartelt
First submitted to arxiv on: 6 Dec 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 This technical report explores the predictive capabilities of various models in completing knowledge graphs. The study compares rule-based approaches with Graph Neural Network (GNN) architectures, specifically NBFNet and A*Net. For two common benchmarks, researchers discovered that a significant portion of the performance difference can be attributed to a unique negative pattern on each dataset that is inaccessible to rule-based methods. This finding sheds new light on the performance disparity between different model classes for knowledge graph completion. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Knowledge graphs are used to represent complex relationships between entities and concepts. To complete these graphs, researchers have developed various models. This study compares two approaches: a rule-based approach and Graph Neural Network (GNN) architectures like NBFNet and A*Net. The results show that GNNs can perform better than rule-based methods for certain tasks. |
Keywords
» Artificial intelligence » Gnn » Graph neural network » Knowledge graph
