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

Autor:	Hoyt, Charles Tapley, Berrendorf, Max, Galkin, Mikhail, Tresp, Volker, Gyori, Benjamin M.
Rok vydání:	2022
Předmět:	Computer Science - Machine Learning Computer Science - Artificial Intelligence
Druh dokumentu:	Working Paper
Popis:	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. Comment: Accepted at the Workshop on Graph Learning Benchmarks @ The WebConf 2022
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2203.07544 Zobrazit plný text záznamu View this record from Arxiv