Rule Mining in Feature Space

Stefano Teso, Andrea Passerini

Invalid Date (modified: Nov 04, 2016) ICLR 2017 conference submission readers: everyone
  • 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.
  • TL;DR: We propose an algorithm to discover logical theories from relational embeddings of knowledge bases.
  • Keywords: Unsupervised Learning
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