Summary of Xlp: Explainable Link Prediction For Master Data Management, by Balaji Ganesan et al.
xLP: Explainable Link Prediction for Master Data Management
by Balaji Ganesan, Matheen Ahmed Pasha, Srinivasa Parkala, Neeraj R Singh, Gayatri Mishra, Sumit Bhatia, Hima Patel, Somashekar Naganna, Sameep Mehta
First submitted to arxiv on: 14 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 approach develops explainability solutions for neural model predictions in enterprise applications, where user trust is crucial for adoption. By drawing from research in interpretability, fact verification, path ranking, neuro-symbolic reasoning, and self-explaining AI, the solution aims to provide creative explanations for link prediction in master data management. The demo showcases how users can choose from various explanations that best suit their preferences. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper creates ways to explain why neural models make certain predictions. This is important because people need to trust these predictions before they will use them. The researchers took ideas from other areas like interpretability, fact checking, and understanding complex paths. They also used AI that can explain itself. The demo shows how users can pick the explanations they prefer. |
