NLP-KAOS for systems goal elicitation: Smart metering system case study

Erik Casagrande, Selamawit Woldeamlak, Wei Lee Woon, H. H. Zeineldin, Davor Svetinovic

Research output: Contribution to journalArticlepeer-review

35 Scopus citations


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.

Original languageBritish English
Article number6857327
Pages (from-to)941-956
Number of pages16
JournalIEEE Transactions on Software Engineering
Issue number10
StatePublished - 1 Oct 2014


  • bibliometrics
  • data mining
  • goal elicitation
  • NLP
  • Requirements engineering


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