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arXiv · 2203.07544

A Unified Framework for Rank-based Evaluation Metrics for Link Prediction in Knowledge Graphs

Charles Tapley Hoyt·Max Berrendorf·Mikhail Galkin·Volker Tresp·Benjamin M. Gyori
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Abstract

The link prediction task on knowledge graphs without explicit negative triples in the training data motivates the usage of rank-based metrics. Here, we review existing rank-based metrics and propose desiderata for improved metrics to address lack of interpretability and comparability of existing metrics to datasets of different sizes and properties. We introduce a simple theoretical framework for rank-based metrics upon which we investigate two avenues for improvements to existing metrics via alternative aggregation functions and concepts from probability theory. We finally propose several new rank-based metrics that are more easily interpreted and compared accompanied by a demonstration of their usage in a benchmarking of knowledge graph embedding models.

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Charles Tapley Hoyt, Max Berrendorf, Mikhail Galkin, Volker Tresp, Benjamin M. Gyori. 2022-03-14. A Unified Framework for Rank-based Evaluation Metrics for Link Prediction in Knowledge Graphs. https://arxiv.org/abs/2203.07544

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