Summary of Logical Reasoning with Relation Network For Inductive Knowledge Graph Completion, by Qinggang Zhang et al.
Logical Reasoning with Relation Network for Inductive Knowledge Graph Completion
by Qinggang Zhang, Keyu Duan, Junnan Dong, Pai Zheng, Xiao Huang
First submitted to arxiv on: 3 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 This research proposes a novel approach called NORAN for inductive knowledge graph completion (KGC), which aims to infer missing relations for new entities not present in the training set. This setting is more realistic, as real-world KGs constantly evolve and introduce new knowledge. The paper addresses two key issues: data sparsity and the cold-start problem, where local information from few neighbors may not be sufficient for accurate reasoning. NORAN centers on relation patterns to capture entity-independent logical evidence, making it suitable for inductive KGC. The framework outperforms state-of-the-art methods on five benchmarks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary NORAN is a new way to complete knowledge graphs when we don’t have much information about new entities. Right now, we can only learn from what we already know, which isn’t very helpful. NORAN helps by finding patterns in how things are related and using that to make predictions about new things. This makes it better at completing knowledge graphs than other methods. |
Keywords
» Artificial intelligence » Knowledge graph
