A Critical Assessment of State-of-the-Art in Entity Alignment

TL;DR

This paper critically evaluates current state-of-the-art entity alignment methods in knowledge graphs, highlighting benchmarking issues, the impact of initialization, and challenges with noisy data, providing a fair evaluation framework.

Contribution

It introduces a standardized evaluation setup for entity alignment, analyzes the influence of hyperparameter tuning practices, and offers insights into the robustness of SotA methods against noise.

Findings

SotA methods outperform baselines generally

Performance drops significantly with noisy datasets

Feature importance varies from previous assumptions

Abstract

In this work, we perform an extensive investigation of two state-of-the-art (SotA) methods for the task of Entity Alignment in Knowledge Graphs. Therefore, we first carefully examine the benchmarking process and identify several shortcomings, which make the results reported in the original works not always comparable. Furthermore, we suspect that it is a common practice in the community to make the hyperparameter optimization directly on a test set, reducing the informative value of reported performance. Thus, we select a representative sample of benchmarking datasets and describe their properties. We also examine different initializations for entity representations since they are a decisive factor for model performance. Furthermore, we use a shared train/validation/test split for a fair evaluation setting in which we evaluate all methods on all datasets. In our evaluation, we make several interesting findings. While we observe that most of the time SotA approaches perform better than baselines, they have difficulties when the dataset contains noise, which is the case in most real-life applications. Moreover, we find out in our ablation study that often different features of SotA methods are crucial for good performance than previously assumed. The code is available at https://github.com/mberr/ea-sota-comparison.