A Neural Network Based Approach to Domain Modelling Relationships and Patterns Recognition

Model-Driven Software Engineering advocates the use of models and their transformations across different stages of software engineering to better understand and analyze systems under development. Domain modelling is used during requirements analysis or the early stages of design to transform informal requirements written in natural language to domain models which are analyzable and more concise. Since domain modelling is time-consuming and requires modelling skills and experience, many approaches have been proposed to extract domain concepts and relationships automatically using extraction rules. However, relationships and patterns are often hidden in the sentences of a problem description. Automatic recognition of relationships or patterns in those cases requires context information and external knowledge of participating domain concepts, which goes beyond what is possible with extraction rules. In this paper, we draw on recent work on domain model extraction and envision a novel technique where sentence boundaries are customized and clusters of sentences are created for domain concepts. The technique further exploits a BiLSTM neural network model to identify relationships and patterns among domain concepts. We also present a classification strategy for relationships and patterns and use it to instantiate our technique. Preliminary results indicate that this novel idea is promising and warrants further research.