Summary of Soft Reasoning on Uncertain Knowledge Graphs, by Weizhi Fei et al.
Soft Reasoning on Uncertain Knowledge Graphs
by Weizhi Fei, Zihao Wang, Hang Yin, Yang Duan, Hanghang Tong, Yangqiu Song
First submitted to arxiv on: 3 Mar 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- 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 The proposed paper advances machine learning-based logical query-answering by considering uncertainty in large-scale and incomplete knowledge graphs. It addresses the gap between uncertain knowledge and traditional first-order logic, using soft queries motivated by soft constraint programming. The approach combines forward inference and backward calibration for answering soft queries on uncertain knowledge graphs. This ML-based method shares the same complexity as state-of-the-art inference algorithms for first-order queries. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps us answer questions about big data by considering how we don’t always know things for sure. It’s like trying to find information in a library where some books are missing or unclear. The researchers developed a new way to ask questions and get answers using machine learning, which is really good at handling incomplete or uncertain information. They tested their approach and found it outperforms previous methods. |
Keywords
» Artificial intelligence » Inference » Machine learning
