Abstract
Link prediction and entailment graph induction are often treated as different problems.
In this paper, we show that these two problems are actually complementary. We train a
link prediction model on a knowledge graph
of assertions extracted from raw text. We
propose an entailment score that exploits the
new facts discovered by the link prediction
model, and then form entailment graphs between relations. We further use the learned entailments to predict improved link prediction
scores. Our results show that the two tasks
can benefit from each other. The new entailment score outperforms prior state-of-the-art
results on a standard entialment dataset and the
new link prediction scores show improvements
over the raw link prediction scores.