title: Rule Mining in Feature Space
abstract: Relational embeddings have emerged as an excellent tool for inferring novel facts
  from partially observed knowledge bases. Recently, it was shown that some
  classes of embeddings can also be exploited to perform a simplified form of rule
  mining. By interpreting logical conjunction as a form of composition between re-
  lation embeddings, simplified logical theories can be mined directly in the space
  of latent representations. In this paper, we present a method to mine full-fledged
  logical theories, which are significantly more expressive, by casting the semantics
  of the logical operators to the space of the embeddings. In order to extract relevant
  rules in the space of relation compositions we borrow sparse reconstruction pro-
  cedures from the field of compressed sensing. Our empirical analysis showcases
  the advantages of our approach.
authors: Stefano Teso Andrea Passerini
hIndex: Andrea Passerini 22