Summary of Replacing Paths with Connection-biased Attention For Knowledge Graph Completion, by Sharmishtha Dutta et al.
Replacing Paths with Connection-Biased Attention for Knowledge Graph Completion
by Sharmishtha Dutta, Alex Gittens, Mohammed J. Zaki, Charu C. Aggarwal
First submitted to arxiv on: 1 Oct 2024
Categories
- Main: Machine Learning (cs.LG)
- 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 paper explores knowledge graph (KG) completion, a task that aims to infer additional facts from existing KG data. The study focuses on the inductive setting, where new entities are introduced at test time, and models must learn to generalize without explicit path information. The authors propose a Transformer-based subgraph encoding module, which incorporates connection-biased attention and entity role embeddings to eliminate the need for expensive path encodings. The CBLiP model outperforms previous models that don’t use path information on standard inductive KG completion benchmarks. While competitive with path-aware models, CBLiP is faster and more efficient. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary KG completion aims to fill in missing facts in a knowledge graph. This study looks at how well models can do this when they’ve never seen the entities before. The researchers designed a new model that doesn’t need to look at all the connections between entities, which makes it faster and easier to use. They tested their model on some standard datasets and found that it did just as well or even better than other models that looked at all those connections. |
Keywords
» Artificial intelligence » Attention » Knowledge graph » Transformer
