Papers › AutoWeird: Weird Translational Scoring Function Identified by Random Search
AutoWeird: Weird Translational Scoring Function Identified by Random Search
Hansi Yang, Yongqi Zhang, Quanming Yao
Scoring function (SF) measures the plausibility of triplets in knowledge graphs. Different scoring functions can lead to huge differences in link prediction performances on different knowledge graphs. In this report, we describe a weird scoring function found by random search on the open graph benchmark (OGB). This scoring function, called AutoWeird, only uses tail entity and relation in a triplet to compute its plausibility score. Experimental results show that AutoWeird achieves top-1 performance on ogbl-wikikg2 data set, but has much worse performance than other methods on ogbl-biokg data set. By analyzing the tail entity distribution and evaluation protocol of these two data sets, we attribute the unexpected success of AutoWeird on ogbl-wikikg2 to inappropriate evaluation and concentrated tail entity distribution. Such results may motivate further research on how to accurately evaluate the performance of different link prediction methods for knowledge graphs.
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Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Link Property Prediction | ogbl-wikikg2 | AutoWeird | Ext. data | No | #2 of 30 | Archive leaderboard | report |
| Link Property Prediction | ogbl-wikikg2 | AutoWeird | Number of params | 19215402 | #2 of 30 | Archive leaderboard | report |
| Link Property Prediction | ogbl-wikikg2 | AutoWeird | Test MRR | 0.7353 ± 0.0006 | #2 of 30 | Archive leaderboard | report |
| Link Property Prediction | ogbl-wikikg2 | AutoWeird | Validation MRR | 0.7362 ± 0.0006 | #2 of 30 | Archive leaderboard | report |
Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.
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