Winograd Schema Challenge (WSC) is a pronoun resolution task which seems to require reasoning with commonsense knowledge.The needed knowledge is not present in the given text.Automatic extraction of the needed knowledge is a bottleneck in solving the challenge.The existing state-of-the-art approach uses the knowledge embedded in their pretrained language model.However, the language models only embed part of the knowledge, the ones related to frequently co-existing concepts.This limits the performance of such models on the WSC problems.In this work, we build-up on the language model based methods and augment them with a commonsense knowledge hunting (using automatic extraction from text) module and an explicit reasoning module.Our end-to-end system built in such a manner improves on the accuracy of two of the available language model based approaches by 5.53% and 7.7% respectively.Overall our system achieves the state-of-theart accuracy of 71.06% on the WSC dataset, an improvement of 7.36% over the previous best.