  Distant supervision is a widely applied approach to automatic training of
relation extraction systems and has the advantage that it can generate large
amounts of labelled data with minimal effort. However, this data may contain
errors and consequently systems trained using distant supervision tend not to
perform as well as those based on manually labelled data. This work proposes a
novel method for detecting potential false negative training examples using a
knowledge inference method. Results show that our approach improves the
performance of relation extraction systems trained using distantly supervised
data.
