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In this work, we take a closer look at the evaluation of two families ofmethods for enriching information from knowledge graphs: Link Prediction andEntity Alignment. In the current experimental setting, multiple differentscores are employed to assess different aspects of model performance. Weanalyze the informativeness of these evaluation measures and identify severalshortcomings. In particular, we demonstrate that all existing scores can hardlybe used to compare results across different datasets. Moreover, we demonstratethat varying size of the test size automatically has impact on the performanceof 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 tothe evaluation and demonstrate empirically how this supports a fair,comparable, and interpretable assessment of model performance. Our code isavailable at https://github.com/mberr/rank-based-evaluation.

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