Unfortunately, populating a universal schema knowledge base using matrix factorization suffers from a problem all distantly-supervised techniques share: you can only reliably learn relations that appear frequently enough in the knowledge base.
for relations that do not appear in the knowledge base or for which no facts are known we cannot learn a predictor at all.