Summary of Look Globally and Reason: Two-stage Path Reasoning Over Sparse Knowledge Graphs, by Saiping Guan et al.
Look Globally and Reason: Two-stage Path Reasoning over Sparse Knowledge Graphs
by Saiping Guan, Jiyao Wei, Xiaolong Jin, Jiafeng Guo, Xueqi Cheng
First submitted to arxiv on: 26 Jul 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI)
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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 Sparse Knowledge Graphs (KGs) with fewer facts are common in real-world applications. The task of completing these sparse KGs by reasoning missing facts is challenging due to limited information. Path-based models, which provide excellent explainability, are often used for this task. However, existing path-based models rely on external models to fill in missing facts and perform path reasoning, introducing unexplainable factors or requiring meticulous rule design. This paper proposes an alternative approach by looking inward instead of seeking external assistance. The proposed LoGRe (Look Globally and Reason) model constructs a relation-path reasoning schema by globally analyzing training data to alleviate the sparseness problem. Experimental results on five benchmark sparse KG datasets demonstrate the effectiveness of the proposed LoGRe model. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Imagine trying to complete a puzzle with many missing pieces. This is similar to what happens when we try to fill in gaps in knowledge graphs, which are like databases that store information about things and their relationships. The problem gets even harder if there aren’t many facts to start with. A team of researchers came up with a new way to tackle this challenge by looking at the data more closely instead of relying on other models. They created a model called LoGRe, which helps fill in missing pieces by analyzing the available information. This approach worked well when tested on different datasets. |
