Capturing client’s needs and expectations for a product or service is an important problem in software development. Software requirements are normally captured in natural language and mostly they are unstructured which makes it difficult to automate the process of going from software requirements to the executable code. A big hurdle in this process is the lack of consistency and standardization in software requirements representation. Thus, the aim of the thesis is to present a method for transforming natural language requirement text into ontology. It is easy to store and retrieve information from ontology as it is a semantic model, and it is also easy to infer new knowledge from it.

As it is clear from the aim of this work, the main component of our research was software requirements, so there was a need to investigate and decide the types of requirements to define the scope of this research. We selected INCOSE guidelines as a benchmark to scrutinize the properties which we desired in the Natural Language Requirements. These natural language requirements were used in the form of user stories as the input of the transformation process. We selected a combination of two methods for our research i.e. Literature Review and Design Science Research. The reason for selecting these methods was to obtain a good grip on existing work going on in this field and then to combine the knowledge to propose new rules for the requirements to ontology transformation. We studied different domains during literature review such as Requirements Engineering, Ontologies, Natural Language Processing, and Information Extraction. The gathered knowledge was then used to propose the rules and the flow of their implementation. This proposed system was named as “Reqtology”. Reqtology defines the process, from taking the requirements in form of user stories, to extracting the useful information based on the rules and then classifying that information so that it can be used to form ontologies. The workflow consists of a 6-step process which starts from input text in form of user stories and at the end provides us entities which can be used for ontologies formation.