A Simple Approach to Case-Based Reasoning in Knowledge Bases
Table 1: Link prediction results on the FB122 dataset.
['[EMPTY]', '[BOLD] Model', 'hits@3', 'hits@5', 'hits@10', 'MRR']
FB122: Next we consider the FB122 dataset by guo2016jointly. Comparing results on FB122 is attractive for a couple of reasons — (a) Firstly, this dataset comes with a set of logical rules hand coded by the authors that can be used for logical inference. It would be interesting to see if our CBR approach is able to automatically uncover the rules from the data. (b) Secondly, there is a recent work on a neural model for logical inference (GNTPs) minervini2019differentiable that scales neural-theorem-provers ntp to this dataset and hence we can directly compare with them. We compare with several baselines. CBR significantly outperforms GNTPs and even outperforms most models which have access to the hand-coded rules during training. We also find that CBR is able to uncover correct rules for 27 out of 31 (87%) query relations.