  Learning knowledge representation is an increasingly important technology
applicable in many domain-specific machine learning problems. We discuss the
effectiveness of traditional Link Prediction or Knowledge Graph Completion
evaluation protocol when embedding knowledge representation for categorised
multi-relational data in the clinical domain. Link prediction uses to split the
data into training and evaluation subsets, leading to loss of information along
training and harming the knowledge representation model accuracy. We propose a
Clustering Evaluation Protocol as a replacement alternative to the
traditionally used evaluation tasks. We used embedding models trained by a
knowledge embedding approach which has been evaluated with clinical datasets.
Experimental results with Pearson and Spearman correlations show strong
evidence that the novel proposed evaluation protocol is pottentially able to
replace link prediction.
