Conference paper
Interpretable and Fair Comparison of Link Prediction or Entity Alignment Methods
Ludwig Maximilian University of Munich1
Statistics and Data Analysis, Department of Applied Mathematics and Computer Science, Technical University of Denmark2
Cognitive Systems, Department of Applied Mathematics and Computer Science, Technical University of Denmark3
Department of Applied Mathematics and Computer Science, Technical University of Denmark4
Siemens5
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. Therefore, we propose adjustments to the evaluation and demonstrate empirically how this supports a fair, comparable, and interpretable assessment of model performance.
Language: | English |
---|---|
Publisher: | IEEE |
Year: | 2020 |
Pages: | 371-74 |
Proceedings: | 2020 IEEE/WIC/ACM International Joint Conference on Web Intelligence and Intelligent Agent Technology (WI-IAT) |
ISBN: | 1665419245 , 9781665419246 , 1665430176 and 9781665430173 |
Types: | Conference paper |
DOI: | 10.1109/WIIAT50758.2020.00053 |
ORCIDs: | Vermue, Laurent |