SnapE -- Training Snapshot Ensembles of Link Prediction Models

Autor:	Shaban, Ali, Paulheim, Heiko
Rok vydání:	2024
Předmět:	Computer Science - Machine Learning Computer Science - Artificial Intelligence
Druh dokumentu:	Working Paper
Popis:	Snapshot ensembles have been widely used in various fields of prediction. They allow for training an ensemble of prediction models at the cost of training a single one. They are known to yield more robust predictions by creating a set of diverse base models. In this paper, we introduce an approach to transfer the idea of snapshot ensembles to link prediction models in knowledge graphs. Moreover, since link prediction in knowledge graphs is a setup without explicit negative examples, we propose a novel training loop that iteratively creates negative examples using previous snapshot models. An evaluation with four base models across four datasets shows that this approach constantly outperforms the single model approach, while keeping the training time constant. Comment: Accepted at International Semantic Web Conference (ISWC) 2024
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2408.02707 Zobrazit plný text záznamu View this record from Arxiv