A Semi-automatic System for Knowledge Base Population

The typical method for transferring key information from unstructured text to knowledge bases is laborious manual entry, but automated information extraction is still at unacceptable accuracies to replace it. A viable alternative is a user interface that allows correction and validation of assertions proposed by the automated extractor for entry into the knowledge base. In this paper, we discuss our system for semi-automatic database population and how issues arising in content extraction and knowledge base population are addressed. The major contributions are detailing challenges in building a semi-automated tool, classifying expected extraction errors, identifying the gaps in current extraction technology with regard to databasing, and designing and developing the FEEDE system that supports human correction of automated content extractors in order to speed up data entry into knowledge bases.