Interpretable and Fair Comparison of Link Prediction or Entity Alignment Methods

Max Berrendorf, Evgeniy Faerman, Laurent Vermue, Volker Tresp

Research output: Chapter in Book/Report/Conference proceedingArticle in proceedingsResearchpeer-review

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. Therefore, we propose adjustments to the evaluation and demonstrate empirically how this supports a fair, comparable, and interpretable assessment of model performance.
Original languageEnglish
Title of host publicationProceedings of 2020 IEEE/WIC/ACM International Joint Conference on Web Intelligence and Intelligent Agent Technology (WI-IAT)
PublisherIEEE
Publication date2020
Pages371-74
ISBN (Print)978-1-6654-3017-3
DOIs
Publication statusPublished - 2020
Event2020 IEEE/WIC/ACM International Joint Conference on Web Intelligence and Intelligent Agent Technology (WI-IAT) - Virtual event
Duration: 14 Dec 202017 Dec 2020
http://wi2020.vcrab.com.au/

Conference

Conference2020 IEEE/WIC/ACM International Joint Conference on Web Intelligence and Intelligent Agent Technology (WI-IAT)
LocationVirtual event
Period14/12/202017/12/2020
Internet address

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