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

TL;DR

This paper critically examines the limitations of current rank-based evaluation metrics for knowledge graph tasks, revealing issues with comparability and interpretation, and proposes adjustments for fairer assessment.

Contribution

It identifies shortcomings of existing evaluation scores and introduces modifications to improve fairness, comparability, and interpretability in model performance assessment.

Findings

Existing scores are inadequate for cross-dataset comparison.

Test size impacts model performance metrics.

Proposed evaluation adjustments improve fairness and interpretability.

Abstract

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.