On the Ambiguity of Rank-Based Evaluation of Entity Alignment or Link Prediction Methods

In this work, we take a closer look at the evaluation of two families of methods for enriching information from knowledge graphs: Link Prediction and Entity Alignment. In the current experimental setting, multiple different scores are employed to assess different aspects of model performance. We analyze the informativeness of these evaluation measures and identify several shortcomings. In particular, we demonstrate that all existing scores can hardly be used to compare results across different datasets. Moreover, we demonstrate that varying size of the test size automatically has impact on the performance of the same model based on commonly used metrics for the Entity Alignment task. We show that this leads to various problems in the interpretation of results, which may support misleading conclusions. Therefore, we propose adjustments to the evaluation and demonstrate empirically how this supports a fair, comparable, and interpretable assessment of model performance. Our code is available at https://github.com/mberr/rank-based-evaluation

Knowledge Base Completion: Baseline strikes back (Again)

Knowledge Base Completion has been a very active area recently, where multiplicative models have generally outperformed additive and other deep learning methods -- like GNN, CNN, path-based models. Several recent KBC papers propose architectural changes, new training methods, or even a new problem reformulation. They evaluate their methods on standard benchmark datasets - FB15k, FB15k-237, WN18, WN18RR, and Yago3-10. Recently, some papers discussed how 1-N scoring can speed up training and evaluation. In this paper, we discuss how by just applying this training regime to a basic model like Complex gives near SOTA performance on all the datasets -- we call this model COMPLEX-V2. We also highlight how various multiplicative methods recently proposed in literature benefit from this trick and become indistinguishable in terms of performance on most datasets. This paper calls for a reassessment of their individual value, in light of these findings