Summary of Neural Probabilistic Logic Learning For Knowledge Graph Reasoning, by Fengsong Sun et al.
Neural Probabilistic Logic Learning for Knowledge Graph Reasoning
by Fengsong Sun, Jinyu Wang, Zhiqing Wei, Xianchao Zhang
First submitted to arxiv on: 4 Jul 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- 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 The proposed Neural Probabilistic Logic Learning (NPLL) framework is designed to achieve accurate knowledge graph (KG) reasoning by striking a balance between model simplicity and reasoning capabilities. The approach introduces a scoring module to enhance the expressive power of embedding networks, while also improving interpretability through Markov Logic Network based variational inference. Evaluation on benchmark datasets shows that NPLL substantially enhances the accuracy and quality of reasoning results. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary NPLL is a new way to reason on large-scale knowledge graphs. It tries to balance being simple with being good at making connections between facts. To do this, it uses a special scoring system that helps its embedding networks make more accurate predictions. NPLL also makes it easier to understand how it works by using something called Markov Logic Networks. This approach was tested on several datasets and showed big improvements in accuracy and quality. |
Keywords
» Artificial intelligence » Embedding » Inference » Knowledge graph
