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Automatic Detection of Semantic Clusters in Glossaries
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Author(s): Marcela Ridao (Universidad Nacional del Centro de la Provincia de Buenos Aires, Argentina)and Jorge Horacio Doorn (Universidad Nacional del Oeste, Argentina & Universidad Nacional de Tres de Febrero, Argentina)
Copyright: 2021
Pages: 17
Source title:
Encyclopedia of Organizational Knowledge, Administration, and Technology
Source Author(s)/Editor(s): Mehdi Khosrow-Pour D.B.A. (Information Resources Management Association, USA)
DOI: 10.4018/978-1-7998-3473-1.ch052
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Abstract
To define the services that a software system will provide, in terms of the business process needs, is a rather important task. As a first step, the requirements engineer must deeply understand the peculiarities of the context in which the future software system will run. There have been proposed several approaches to elicit and model such knowledge. The research whose main results are presented in this chapter, was carried out in a process that models all the information acquired in natural language. The advantages of the use of natural language are somehow shadowed by its ambiguity and the way models are arranged. This chapter deals with some techniques developed to visualize hidden information in this sort of models. This is mainly done by means of the use of directed graphs and the finding of clusters on them. This chapter enhances a previous one, by improving the visualization technique and by adding an automatic cluster detection technique.
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