  The aim of knowledge base completion is to predict unseen facts from existing
facts in knowledge bases. In this work, we introduce the first approach for
transfer of knowledge from one collection of facts to another without the need
for entity or relation matching. The method works for both canonicalized
knowledge bases and uncanonicalized or open knowledge bases, i.e., knowledge
bases where more than one copy of a real-world entity or relation may exist.
Such knowledge bases are a natural output of automated information extraction
tools that extract structured data from unstructured text. Our main
contribution is a method that can make use of a large-scale pre-training on
facts, collected from unstructured text, to improve predictions on structured
data from a specific domain. The introduced method is the most impactful on
small datasets such as ReVerb20K, where we obtained 6% absolute increase of
mean reciprocal rank and 65% relative decrease of mean rank over the previously
best method, despite not relying on large pre-trained models like BERT.
