Summary of Context-aware Inductive Knowledge Graph Completion with Latent Type Constraints and Subgraph Reasoning, by Muzhi Li et al.
Context-aware Inductive Knowledge Graph Completion with Latent Type Constraints and Subgraph Reasoning
by Muzhi Li, Cehao Yang, Chengjin Xu, Zixing Song, Xuhui Jiang, Jian Guo, Ho-fung Leung, Irwin King
First submitted to arxiv on: 22 Oct 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Computation and Language (cs.CL)
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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 A novel approach to inductive knowledge graph completion (KGC) is proposed, which addresses the limitations of existing methods that rely heavily on reasoning paths between head and tail entities. The CATS model activates large language models’ semantic understanding and reasoning capabilities using type-aware and subgraph reasoning modules. By incorporating latent type constraints and neighboring facts, CATS significantly outperforms state-of-the-art methods in 16 out of 18 transductive, inductive, and few-shot settings, with an average absolute MRR improvement of 7.2%. This breakthrough has the potential to improve knowledge graph completion tasks in various scenarios. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary A new way to complete missing information in knowledge graphs is introduced. The CATS model uses special techniques to help large language models understand what’s important and make good guesses about missing data. This helps it do better than other methods in many different situations. The results are impressive, with significant improvements over the best previous approaches. |
Keywords
» Artificial intelligence » Few shot » Knowledge graph
