  During the past few decades, knowledge bases (KBs) have experienced rapid
growth. Nevertheless, most KBs still suffer from serious incompletion.
Researchers proposed many tasks such as knowledge base completion and relation
prediction to help build the representation of KBs. However, there are some
issues unsettled towards enriching the KBs. Knowledge base completion and
relation prediction assume that we know two elements of the fact triples and we
are going to predict the missing one. This assumption is too restricted in
practice and prevents it from discovering new facts directly. To address this
issue, we propose a new task, namely, fact discovery from knowledge base. This
task only requires that we know the head entity and the goal is to discover
facts associated with the head entity. To tackle this new problem, we propose a
novel framework that decomposes the discovery problem into several facet
discovery components. We also propose a novel auto-encoder based facet
component to estimate some facets of the fact. Besides, we propose a feedback
learning component to share the information between each facet. We evaluate our
framework using a benchmark dataset and the experimental results show that our
framework achieves promising results. We also conduct extensive analysis of our
framework in discovering different kinds of facts. The source code of this
paper can be obtained from https://github.com/thunlp/FFD.
