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Obtaining E-R diagrams semi-automatically from natural language specifications

Meziane, F and Vadera, S 2004, Obtaining E-R diagrams semi-automatically from natural language specifications , in: Sixth International Conference on Enterprise Information Systems (ICEIS 2004), 14-17 April 2004, Universidade Portucalense, Porto, Portugal.

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    Abstract

    Since their inception, entity relationship models have played a central role in systems speci�cation, analysis and development. They have become an important part of several development methodologies and standards such as SSADM. Obtaining entity relationship models, can however, be a lengthy and time consuming task for all but the very smallest of speci�cations. This paper describes a semi-automatic approach for obtaining entity relationship models from natural language speci�cations. The approach begins by using natural language analysis techniques to translate sentences to a meaning representation language called logical form language. The logical forms of the sentences are used as a basis for identifying the entities and relationships. Heuristics are then used to suggest suitable degrees for the identi�ed relationships. This paper describes and illustrates the main phases of the approach and presents a summary of the results obtained when it is applied to a case study.

    Item Type: Conference or Workshop Item (Poster)
    Additional Information: In volume 1 of conference proceedings ('Databases and information systems integration')
    Uncontrolled Keywords: Software engineering, entity relationship models, specifcations, natural language processing
    Themes: Subjects / Themes > Q Science > QA Mathematics > QA075 Electronic computers. Computer science
    Subjects outside of the University Themes
    Schools: Colleges and Schools > College of Science & Technology
    Colleges and Schools > College of Science & Technology > School of Computing, Science and Engineering
    Colleges and Schools > College of Science & Technology > School of Computing, Science and Engineering > Data Mining and Pattern Recognition Research Centre
    Refereed: Yes
    Related URLs:
    Depositing User: Prof Farid Meziane
    Date Deposited: 02 Mar 2009 14:58
    Last Modified: 20 Aug 2013 16:56
    URI: http://usir.salford.ac.uk/id/eprint/1805

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