Improving learning and inference in a large knowledge-base using latent syntactic cues

Automatically constructed Knowledge Bases (KBs) are often incomplete and there is a gen-uine need to improve their coverage. Path Ranking Algorithm (PRA) is a recently pro-posed method which aims to improve KB cov-erage by performing inference directly over the KB graph. For the first time, we demon-strate that addition of edges labeled with la-tent features mined from a large dependency parsed corpus of 500 million Web documents can significantly outperform previous PRA-based approaches on the KB inference task. We present extensive experimental results val-idating this finding. The resources presented in this paper are publicly available.