We are interested in building systems that can understand and reason with information in natural language. Our current work focuses on grade science exams as a test domain, where we target extraction of knowledge that can readily support new inferences. We've explored text-derived first-order rules with Markov Logic Networks as a reasoning framework [AKBC 2014, EMNLP 2015], and semantic role based knowledge for recognizing process instances [EMNLP 2016]. These efforts are in close collaboration along with financial support from the Allen Institute for Artificial Intelligence.