Learning Attention-based Embeddings for Relation Prediction in Knowledge Graphs
Table 4: Mean PageRank ×10−5 vs relative increase in MRR wrt. DistMult.
['[BOLD] Dataset', '[BOLD] PageRank', '[BOLD] Relative Increase']
PageRank Analysis : We hypothesize that complex and hidden multi-hop relations among entities are captured more succinctly in dense graphs as opposed to sparse graphs. To test this hypothesis, we perform an analysis similar to ConvE, where they study the correlation between mean PageRank and increase in MRR relative to DistMult. We notice a strong correlation coefficient of r=0.808. We observe an anomaly to our observed correlation in case of NELL-995 versus WN18RR and attribute this to the highly sparse and hierarchical structure of WN18RR which poses as a challenge to our method that does not capture information in a top-down recursive fashion.