Orthogonal Relation Transforms with Graph Context Modeling for Knowledge Graph Embedding
Table 3: Ablation study on FB15k-237 validation set.
['Model', '[ITALIC] ds', 'MRR', '@10', '#param']
Orthogonal transform has many desired properties, for example, the inverse matrix is obtained by simply transposing itself. It also preserves the L2 norm of a vector after the transform. For our work, we are just interested in its property to obtain inverse matrix by simple transposing. We perform the ablation study with embedding dimension of 400. The entity embedding dimension for RotatE-S and RotatE-L are 400 and 2000, respectively. The model size of OTE is less than one-third of the size of RotatE-L but with better performance. This shows the effectiveness of the OTE. Sub-embedding dimension size Is the larger size always better? We fix the entity embedding dimension as 400, and vary the sub-embedding size from 2,5,10,20,50, all the way to 100. The blue line and green bar represent MRR and H@10 value, respectively.