Источник
AISTATS
Дата публикации
02.05.2025
Авторы
Вячеслав Юсупов
Максим Рахуба
Евгений Фролов
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Knowledge graph completion with mixed geometry tensor factorization
Аннотация
In this paper, we propose a new geometricapproach for knowledge graph completionvia low rank tensor approximation. We augmenta pretrained and well-established Euclideanmodel based on a Tucker tensor decompositionwith a novel hyperbolic interactionterm. This correction enables more nuancedcapturing of distributional propertiesin data better aligned with real-world knowledgegraphs. By combining two geometriestogether, our approach improves expressivityof the resulting model achieving new stateof-the-art link prediction accuracy with a significantlylower number of parameters comparedto the previous Euclidean and hyperbolicmodels.