We propose to perform joint inference upon multiple local predictions by leveraging implicit clues that are encoded with relation specific requirements and can be learnt from existing knowledge bases.
the joint inference framework operates on the output of a sentence level relation extractor as input, derives 5 types of constraints from an existing KB to implicitly capture the expected type and cardinality requirements for a relation's arguments, and jointly resolve the disagreements among candidate predictions.