EM-RBR: a reinforced framework for knowledge graph completion from
  reasoning perspective
  Knowledge graph completion aims to predict the new links in given entities
among the knowledge graph (KG). Most mainstream embedding methods focus on fact
triplets contained in the given KG, however, ignoring the rich background
information provided by logic rules driven from knowledge base implicitly. To
solve this problem, in this paper, we propose a general framework, named
EM-RBR(embedding and rule-based reasoning), capable of combining the advantages
of reasoning based on rules and the state-of-the-art models of embedding.
EM-RBR aims to utilize relational background knowledge contained in rules to
conduct multi-relation reasoning link prediction rather than superficial vector
triangle linkage in embedding models. By this way, we can explore relation
between two entities in deeper context to achieve higher accuracy. In
experiments, we demonstrate that EM-RBR achieves better performance compared
with previous models on FB15k, WN18 and our new dataset FB15k-R, especially the
new dataset where our model perform futher better than those state-of-the-arts.
We make the implementation of EM-RBR available at
https://github.com/1173710224/link-prediction-with-rule-based-reasoning.
