Natural Language–Based Conceptual Modelling Frameworks: State of the Art and Future Opportunities

Abstract

Identifying requirements for an information system is an important task and conceptual modelling is the first step in this process. Conceptual modelling plays a critical role in the information system design process and usually involves domain experts and knowledge engineers who brainstorm together to identify the required knowledge to build an information system. The conceptual modelling process starts with the collection of necessary information from the domain experts by the knowledge engineers. Afterwards, the knowledge engineers use traditional model driven engineering techniques to design the system based on the collected information. Natural language based conceptual modelling frameworks or systems are used to help domain experts and knowledge engineers in eliciting requirements and building conceptual models from a natural language text. In this paper, we discuss the state of the art of some recent conceptual modelling frameworks that are based on natural language. We take a closer look at how these frameworks are built, in particular at the underlying motivation, architecture, types of natural language used (e.g., restricted vs unrestricted), types of the conceptual model generated, verification support of the requirements specifications as well as the conceptual models, and underlying knowledge representation formalism. We also discuss some future research opportunities that these frameworks offer.

Gunning Fog Index and Automated UML Generation

“…The automation of UML diagram generation from natural language has seen progress, yet precision and recall challenges persist [13][14][15]. Studies by Dawood et al, Hossain and Rahman, More and Phalnikar, Arman and Jabbarin, and Maatuk and Abdelnabi present diverse NLP-based approaches for UML diagram generation [16][17][18][19][20].…”