Reviews: DRUM: End-To-End Differentiable Rule Mining On Knowledge Graphs

Authors propose DRUM, an end-to-end differentiable rule-based inference method which can be used for mining rules via backprop, and extracting rules from data. Their approach is quite interesting - it can be trained from positive examples only, without negative sampling (this is currently a burden for representation learning algorithms targeting knowledge graphs). In DRUM, paths in a knowledge graph are represented by a chain of matrix multiplications (this idea is not especially novel - see [1]). For mining rules, authors start from a formulation of the problem where each rule is associated with a confidence weight, and try to maximise the likelihood of training triples by optimising an end-to-end differentiable objective. However, the space of possible rules (and thus the number of parameters as confidence scores) is massive, so authors propose a way of efficiently approximating the rule scores tensor using with another having a lower rank (Eq.