Domain Specific Facts Extraction Using Weakly Supervised Active Learning Approach

An ontology is defined using concepts and relationships between the concepts. In this paper, we focus on second problem: relation extraction from plain text. Generic Knowledge Bases like YAGO, Freebase, and DBPedia have made accessible huge collections of facts and their properties from various domains. But acquiring and maintaining various facts and their relations from domain specific corpus becomes very important and challenging task due to low availability of annotated data. Here, we proposed a label propagation based semi-supervised approach for relation extraction by choosing most informative instances for annotation. We also proposed weakly supervised approach for data annotation using generic ontologies like Freebase, which further reduces the cost of annotating data manually. We checked efficiency of our approach by performing experiments on various domain specific corpora.