Relations Expansion: Extracting Relationship Instances from the Web

Abstract

In this paper, we propose a Relation Expansion framework, which uses a few seed sentences marked up with two entities to expand a set of sentences containing target relations. During the expansion process, label propagation algorithm is used to select the most confident entity pairs and context patterns. The label propagation algorithm is a graph based semi-supervised learning method which models the entire data set as a weighted graph and the label score is propagated on this graph. We test the proposed framework with four relationships, the results show that the label propagation is quite competitive comparing with existing methods.