This paper proposes a novel semiautomatic system domain data analysis method. The method is based on the iterative acquisition and analysis of a large body of bibliometric data, generation of domain taxonomies, and creation of domain models. The method was applied on a smart grid case study through collection and analysis of more than 6000 documents. We have found that our method produces domain models of comparable quality to the traditional manually produced domain models in a more cost-effective way.

This paper presents a computational method that employs Natural Language Processing (NLP) and text mining techniques to support requirements engineers in extracting and modeling goals from textual documents. We developed a NLP-based goal elicitation approach within the context of KAOS goal-oriented requirements engineering method. The hierarchical relationships among goals are inferred by automatically building taxonomies from extracted goals. We use smart metering system as a case study to investigate the proposed approach. Smart metering system is an important subsystem of the next generation of power systems (smart grids). Goals are extracted by semantically parsing the grammar of goal-related phrases in abstracts of research publications. The results of this case study show that the developed approach is an effective way to model goals for complex systems, and in particular, for the research-intensive complex systems.