# AutoWeird: Weird Translational Scoring Function Identified by Random Search

arXiv: https://arxiv.org/abs/2207.11673 · HTML: https://mlsanity.com/paper/2207.11673/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 13 of its 18 references that are in the map.
- First posted on arXiv 24 Jul 2022.

## Builds on (most cited first, 13 of 13)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Open Graph Benchmark: Datasets for Machine Learning on Graphs](https://mlsanity.com/paper/2005.00687/) (2020)
- [RotatE: Knowledge Graph Embedding by Relational Rotation in Complex Space](https://mlsanity.com/paper/1902.10197/) (2019)
- [A Survey on Knowledge Graphs: Representation, Acquisition and Applications](https://mlsanity.com/paper/2002.00388/) (2020)
- [Knowledge Graph Completion via Complex Tensor Factorization](https://mlsanity.com/paper/1702.06879/) (2017)
- [NodePiece: Compositional and Parameter-Efficient Representations of Large Knowledge Graphs](https://mlsanity.com/paper/2106.12144/) (2021)
- [PairRE: Knowledge Graph Embeddings via Paired Relation Vectors](https://mlsanity.com/paper/2011.03798/) (2020)
- [Automated Machine Learning: From Principles to Practices](https://mlsanity.com/paper/1810.13306/) (2018)
- [Relation Prediction as an Auxiliary Training Objective for Improving Multi-Relational Graph Representations](https://mlsanity.com/paper/2110.02834/) (2021)
- [AutoSF: Searching Scoring Functions for Knowledge Graph Embedding](https://mlsanity.com/paper/1904.11682/) (2019)
- [Bilinear Scoring Function Search for Knowledge Graph Learning](https://mlsanity.com/paper/2107.00184/) (2021)
- [TranS: Transition-based Knowledge Graph Embedding with Synthetic Relation Representation](https://mlsanity.com/paper/2204.08401/) (2022)
- [InterHT: Knowledge Graph Embeddings by Interaction between Head and Tail Entities](https://mlsanity.com/paper/2202.04897/) (2022)

## Abstract (arXiv)

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.