Summary of Few-shot Knowledge Graph Relational Reasoning Via Subgraph Adaptation, by Haochen Liu et al.
Few-shot Knowledge Graph Relational Reasoning via Subgraph Adaptation
by Haochen Liu, Song Wang, Chen Chen, Jundong Li
First submitted to arxiv on: 19 Jun 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Machine Learning (cs.LG)
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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 few-shot knowledge graph relational reasoning approach is proposed to predict unseen triplets for rare relations in knowledge graphs, given only a few reference triplets. This task has gained attention due to the widespread use of knowledge graphs in natural language processing applications. Previous approaches have used meta-training methods and manually constructed meta-relation sets, while recent efforts focus on edge-mask-based methods that exploit graph structure. However, these methods have limitations in extracting information from knowledge graphs and are influenced by spurious information. The proposed SAFER approach adapts contextualized graph information to various subgraphs generated from support and query triplets for prediction. Experimental results on three datasets demonstrate the superiority of SAFER. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary A new way is found to predict unknown relationships in large collections of information. This helps computers better understand natural language processing tasks like question answering and text summarization. Previous methods tried to solve this problem by using special training data, but they had limitations. The new method, called SAFER, uses a different approach that works better. It takes into account the structure of the relationships in the collection and ignores misleading information. Tests show that SAFER is more accurate than previous methods. |
Keywords
» Artificial intelligence » Attention » Few shot » Knowledge graph » Mask » Natural language processing » Question answering » Summarization
