  Distantly supervised relation extraction has been widely used to find novel
relational facts from plain text. To predict the relation between a pair of two
target entities, existing methods solely rely on those direct sentences
containing both entities. In fact, there are also many sentences containing
only one of the target entities, which provide rich and useful information for
relation extraction. To address this issue, we build inference chains between
two target entities via intermediate entities, and propose a path-based neural
relation extraction model to encode the relational semantics from both direct
sentences and inference chains. Experimental results on real-world datasets
show that, our model can make full use of those sentences containing only one
target entity, and achieves significant and consistent improvements on relation
extraction as compared with baselines. The source code of this paper can be
obtained from https: //github.com/thunlp/PathNRE.
