Summary of Reevaluation Of Inductive Link Prediction, by Simon Ott et al.
Reevaluation of Inductive Link Prediction
by Simon Ott, Christian Meilicke, Heiner Stuckenschmidt
First submitted to arxiv on: 30 Sep 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 A novel study reveals significant flaws in the current evaluation protocol for inductive link prediction, which is based on ranking true entities among a small set of randomly sampled negative entities. This protocol’s limitations allow simple rule-based baselines to achieve state-of-the-art results by prioritizing entities with valid types. To address these issues, the authors reevaluate existing approaches for inductive link prediction on multiple benchmarks using the standard transductive setting. Additionally, they propose and apply an improved sampling protocol that overcomes scalability problems encountered by some inductive methods. The resulting evaluation outcomes differ substantially from previously reported results. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary A team of researchers found a big problem with how we evaluate predicting links between things (like people or places). Currently, we compare the real link to a few fake options and see which one is most likely correct. But this method has a major flaw: it’s too easy! A simple rule that just looks at the type of thing can actually do better than more complex methods. To fix this, the team retested some popular approaches on different data sets using a more standard way of evaluating link prediction. They also came up with a new way to pick which fake options to use that doesn’t have these same problems. The results were very different from what we thought before. |
