Iteratively Learning Embeddings and Rules for Knowledge Graph Reasoning
Table 5. Link prediction results with MRR and Hit@n on WN18RR-sparse and FB15k-237-sparse. Underlined scores are the better ones between ANALOGY and IterE(ANALOGY). Boldface scores are the best results among all methods.
['[EMPTY]', '[BOLD] WN18-sparse MRR', '[BOLD] WN18-sparse MRR', '[BOLD] WN18-sparse Hit@1', '[BOLD] WN18-sparse Hit@3', '[BOLD] WN18-sparse Hit10', '[BOLD] FB15k-sparse MRR', '[BOLD] FB15k-sparse MRR', '[BOLD] FB15k-sparse Hit@1', '[BOLD] FB15k-sparse Hit@3', '[BOLD] FB15k-sparse Hit10']
Results and analysis To show how axiom helps sparse entity embeddings, we adopt two strategies: (1) (2) Secondly, we evaluate how axioms can help improve sparse entity link prediction utilizing its deductive ability. In IterE+axioms, if the test triple (s,r,o) are inferred by axioms during axiom injection, which means (s,r,o)∈Taxiom , we regard it as correct and mark its prediction rank as 1. We analyze the results as follows: Firstly, the link prediction results of IterE competitive to ANALOGY, which means most of the triples injected into embedding learning are not noise, indicating that learning axioms from embeddings works well. Secondly, IterE outperforms baselines on WN18RR-sparse and FB15k-237-sparse, while are slightly improved on WN18-sparse and FB15k-sparse. These indicate that IterE helps sparse entities much more in WN18RR-sparse and FB15k-237-sparse than in WN18RR-sparse and FB15k-sparse. This is quite reasonable in our opinion, because among these four datasets, WN18RR-sparse and FB15k-237-sparse are more challenging and more sparse. Thirdly, the results of IterE+axioms are improved compared with IterE in all datasets, especially on the most complex and sparse dataset FB15k-237-sparse. It indicates that the deductive capability of axioms can help the prediction of sparse entities further.