Software requirement validation is the process by which analysts check, prioritize, and optimize software requirement sentences. Some analysts still manage software requirements manually, for example, by marking conflicts on inconsistent software requirements when managing inconsistencies. However, the manual approach has several weaknesses, including analyst-induced human errors, which causes ambiguity and inefficiency. Therefore, research on developing an automatic software requirement management method is needed to help analysts prevent inefficiency and human error. Various research approaches have been carried out to create this solution, including ontology-based approaches, but most of the research examines how the system manages software requirement between knowledge sources (domain ontology), rather than solely focusing on developing and managing among software requirements (problem ontology). This study focuses on problem ontology, specifically on transforming software requirement based on the Requirement Boilerplates pattern into ontology components using NLP. This transformation consists of two processes: extracting requirement sentences into categorized word collections and transforming categorized word collections into ontology components. The evaluation is conducted using performance metrics to measure the precision and recall of the extraction and transformation results from both processes. By applying this method, analysts can manage software requirements more efficiently and accurately, enhancing the quality and reliability of the software.