Summary of A Pluggable Common Sense-enhanced Framework For Knowledge Graph Completion, by Guanglin Niu et al.
A Pluggable Common Sense-Enhanced Framework for Knowledge Graph Completion
by Guanglin Niu, Bo Li, Siling Feng
First submitted to arxiv on: 6 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 This novel framework addresses the limitations of existing knowledge graph completion (KGC) approaches by incorporating both factual triples and common sense. The proposed pluggable framework is adaptable to different knowledge graphs based on their entity concept richness, allowing for automatic generation of explicit or implicit common sense from factual triples. To further improve performance, a coarse-to-fine inference approach is introduced for KGs with rich entity concepts, while a dual scoring scheme is employed for KGs without concepts. The framework can be integrated as a pluggable module for many knowledge graph embedding models, facilitating joint common sense and fact-driven training and inference. Experimental results demonstrate the framework’s good scalability and superiority over existing models across various KGC tasks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary A team of researchers developed a new way to fill in missing information in a big database called a knowledge graph. They wanted to make sure their answers made sense, not just based on facts, but also on common sense. Their approach combines both types of information and can work with different kinds of databases. They tested it and found that it performed better than other methods. |
Keywords
» Artificial intelligence » Embedding » Inference » Knowledge graph
