  Relation extraction is the task of identifying predefined relationship
between entities, and plays an essential role in information extraction,
knowledge base construction, question answering and so on. Most existing
relation extractors make predictions for each entity pair locally and
individually, while ignoring implicit global clues available across different
entity pairs and in the knowledge base, which often leads to conflicts among
local predictions from different entity pairs. This paper proposes a joint
inference framework that employs such global clues to resolve disagreements
among local predictions. We exploit two kinds of clues to generate constraints
which can capture the implicit type and cardinality requirements of a relation.
Those constraints can be examined in either hard style or soft style, both of
which can be effectively explored in an integer linear program formulation.
Experimental results on both English and Chinese datasets show that our
proposed framework can effectively utilize those two categories of global clues
and resolve the disagreements among local predictions, thus improve various
relation extractors when such clues are applicable to the datasets. Our
experiments also indicate that the clues learnt automatically from existing
knowledge bases perform comparably to or better than those refined by human.
