Numerical Literals in Link Prediction: A Critical Examination of Models and Datasets

Moritz Blum, Basil Ell, Hannes Ill, Philipp Cimiano

Abstract: Link Prediction (LP) is an essential task over Knowledge Graphs (KGs), traditionally focussed on using and predicting the relations between entities. Textual entity descriptions have already been shown to be valuable, but models that incorporate numerical literals have shown minor improvements on existing benchmark datasets. It is unclear whether a model is actually better in using numerical literals, or better capable of utilizing the graph structure. This raises doubts about the effectiveness of these methods and about the suitability of the existing benchmark datasets.