Summary of Linklogic: a New Method and Benchmark For Explainable Knowledge Graph Predictions, by Niraj Kumar-singh et al.
LinkLogic: A New Method and Benchmark for Explainable Knowledge Graph Predictions
by Niraj Kumar-Singh, Gustavo Polleti, Saee Paliwal, Rachel Hodos-Nkhereanye
First submitted to arxiv on: 2 Jun 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Social and Information Networks (cs.SI)
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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 This paper addresses the issue of black box link prediction models in knowledge graphs, which makes it difficult for users to understand model reasoning and make informed decisions about predictions. To overcome this limitation, the authors propose LinkLogic, a simple method that surfaces and ranks explanatory information used for link prediction. The paper also introduces the first-ever link prediction explanation benchmark based on family structures present in the FB13 dataset. This benchmark is used to evaluate LinkLogic quantitatively and qualitatively, assessing its fidelity, selectivity, and relevance. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary LinkLogic is a new method that helps people understand why link prediction models make certain predictions. The authors also created a special test set based on family relationships in the FB13 dataset to see how well LinkLogic works. They tested LinkLogic using this benchmark to check its explanations are good, relevant, and easy to understand. |
