Summary of Conformalized Answer Set Prediction For Knowledge Graph Embedding, by Yuqicheng Zhu et al.
Conformalized Answer Set Prediction for Knowledge Graph Embedding
by Yuqicheng Zhu, Nico Potyka, Jiarong Pan, Bo Xiong, Yunjie He, Evgeny Kharlamov, Steffen Staab
First submitted to arxiv on: 15 Aug 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 Knowledge graph embeddings (KGE) are machine learning models trained on knowledge graphs (KGs) to provide non-classical reasoning capabilities. Typically, KGE methods rank potential answers based on similarities and analogies, but these rankings lack a meaningful probabilistic interpretation, making it challenging to quantify uncertainty in predictions. To address this issue, we apply conformal prediction theory to generate answer sets with probabilistic guarantees for link prediction tasks. Our empirical evaluation on four benchmark datasets using six representative KGE methods validates that the generated answer sets satisfy the theoretical guarantees and often have a sensible size that adapts well to query difficulty. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Imagine you’re searching for information online, but instead of getting a list of results, you get a ranked list of possible answers. These rankings are useful, but they don’t tell you how likely each answer is to be true. This makes it hard to use these models in situations where accuracy matters, like medicine. To fix this problem, we developed a new way to generate lists of possible answers that come with a probability of being correct. We tested our method on four different datasets and six different KGE methods, showing that it works well and provides useful results. |
Keywords
» Artificial intelligence » Knowledge graph » Machine learning » Probability
