Machine Reading (MR) is an art of understanding text by the machine and one of the best tools to evaluate the understanding level of the machine is Reading Comprehension System (RCS) with Multiple Choice Questions (MCQ). In this paper, we proposed with a new knowledge representation, for understanding the given text, called Linguistic Knowledge Document (LKD). Such, LKD is generated from the given comprehension text. Natural Logic is used for generating the LKD. It is like an inference engine which contains all possible inference for each sentence in the comprehension text. The proposed LKD is acting like a human brain for the machine for answering the questions inquired by MCQA system. We use token based alignment model for finding answers from the LKD. We evaluate our system on RACE dataset and the obtained results are compared with recent methods. The comparison results show that the proposed model outperforms the recent results.