Summary of Are We Wasting Time? a Fast, Accurate Performance Evaluation Framework For Knowledge Graph Link Predictors, by Filip Cornell et al.
Are We Wasting Time? A Fast, Accurate Performance Evaluation Framework for Knowledge Graph Link Predictors
by Filip Cornell, Yifei Jin, Jussi Karlgren, Sarunas Girdzijauskas
First submitted to arxiv on: 25 Jan 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Machine Learning (cs.LG)
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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 paper presents a thorough analysis of the limitations of standard evaluation protocols for Knowledge Graph Completion methods, specifically the task of inferring new links to be added to a graph. The authors show that previous approaches using random sampling have serious limitations and can vastly overestimate the ranking performance of a method due to the effect of easy versus hard negative candidates. To mitigate this issue, they propose a framework that uses relational recommenders to guide the selection of candidates for evaluation, which can reduce the time and computation needed while providing accurate estimations of the full, filtered ranking. The authors demonstrate the effectiveness of their methodology on the ogbl-wikikg2 dataset, showing that simple and fast methods can match advanced neural approaches even when a large portion of true candidates are missed. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about fixing a problem in how we evaluate computer programs that fill in gaps in our knowledge about the world. These programs, called Knowledge Graph Completion methods, help us make predictions based on what we already know. Right now, we use a method that involves ranking all the things we know to see which ones fit best with new information. But this takes a long time and isn’t very accurate. The authors of this paper came up with a better way to do it using something called relational recommenders. This new approach is much faster and more accurate, even when we don’t have complete information. It’s like having a personal assistant that helps you find the right answer quickly! |
Keywords
* Artificial intelligence * Knowledge graph
