ABSTRACT. Knowledge acquisition from text is process of automatically acquiring, organizing and structuring knowledge from text which can be used to perform question answering or complex reasoning. However, current state-of-the-art systems are limited by the fact that they are not able to construct the knowledge base with high quality as knowledge representation and reasoning (KRR) has a keen requirement for the accuracy of data. Controlled Natural Languages (CNLs) emerged as a way to improve the quality problem by restricting the flexibility of the language. However, they still fail to do so as sentences that express the same information may be represented by different forms. Current CNL systems have limited power to standardize the sentences into the same logical form. We solved this problem by building the Knowledge Acquisition Logic Machine (KALM), which performs semantic analysis of English sentences and achieves superior accuracy of standardizing sentences that express the same meaning to the same logical representation. Besides, we developed the query part of KALM to perform question answering, which also achieves very high accuracy in query understanding.