Table 1.

Model performance results for general relation prediction. The table shows the test results of several competing KG embedding methods, including TransE, RotatE, ComplEx, DistMult, DeepWalk and node2vec, as well as our two tested MultiGML model variants. Best results are marked in bold. Both RGCN and RGAT variants of the MultiGML model were tested with two types of input features. The model variant “multimodal” refers to the use of several modalities for each node type described in section 3.1.2. In the model variant “basic” all input features have been initialized with the Xavier-Glorot method, i.e. the model effectively learns from the topology only.

Model	AUROC	AUPR
TransE	0.667	0.633
RotatE	0.793	0.759
ComplEx	0.757	0.699
DistMult	0.765	0.696
DeepWalk	0.648	0.622
Node2Vec	0.807	0.794
MultiGML-RGCN (basic)	0.847	0.787
MultiGML-RGAT (basic)	0.843	0.793
MultiGML-RGCN (multimodal)	0.859	0.808
MultiGML-RGAT (multimodal)	0.845	0.798