Graph Pattern Entity Ranking Model for Knowledge Graph Completion
Table 2: Mean Reciprocal Rank (MRR) and HITS@n scores obtained for the link prediction tasks on the WN18, FB15k, WN18RR, and FB15k-237 datasets. The highest result for each column is shown in bold. The results of TransE and TorusE were reported by Ebisu and Ichise Ebisu and Ichise (2018), the results of RESCAL were reported by Nickel et al. Nickel et al. (2016), the results of DistMult and ComplEx were reported by Trouillon et al. Trouillon et al. (2016), the results of R-GCN and ConvE were reported by Dettmers et al. Dettmers et al. (2018), the results of PRA were reported by Liu et al. Liu et al. (2016), and the results of Node+LinkFeat were reported by Toutanova and Chen Toutanova and Chen (2015).
['[EMPTY]', 'WN18 MRR', 'WN18 HITS@', 'WN18 HITS@', 'WN18 HITS@', 'FB15k MRR', 'FB15k HITS@', 'FB15k HITS@', 'FB15k HITS@', 'WN18RR MRR', 'WN18RR HITS@', 'WN18RR HITS@', 'WN18RR HITS@', 'FB15k-237 MRR', 'FB15k-237 HITS@', 'FB15k-237 HITS@', 'FB15k-237 HITS@']
The Node+LinkFeat model performed well on WN18 and FB15k because these datasets often contain the reverse relations of other relations. In other words, it shows that knowledge graph embedding models failed to capture this redundancy. On the other hand, our proposed models, GPro and GRank, generally yield better results than the knowledge graph embedding models and results which are better than or comparable to Node+LinkFeat, which means that our models can also handle such redundancy. In particular, GRank with dMAP and fdMAP yielded the best results on FB15k. This indicates that taking the multiplicity of matchings and deeper information into account is important for knowledge graphs such as FreeBase that contain miscellaneous relations and are not well curated like WordNet. As a result, GRank performed well. For FB15k-237, the performance of Node+LinkFeat is comparable with most of the other more sophisticated knowledge graph models and GPro does not yield good results because FB15k-237 has less redundancy. GRank also performs better than the most other models for the FB15k-237 dataset for the same reason as the FB15k dataset. However, our models do not utilize the information related to the co-occurrence of entities and relations in triples (node features Toutanova and Chen We also limited the size and the shapes of graph patterns because of the calculation time; we will address these and improve our models further in our future work.