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Semi-Automated Development of Conceptual Models from Natural Language Text

Omar, Mussa (2018) Semi-Automated Development of Conceptual Models from Natural Language Text. Doctoral thesis, University of Huddersfield.

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Abstract

The process of converting natural language specifications into conceptual models requires detailed analysis of natural language text, and designers frequently make mistakes when undertaking this transformation manually. Although many approaches have been used to help designers translate natural language text into conceptual models, each approach has its limitations. One of the main limitations is the lack of a domain-independent ontology that can be used as a repository for entities and relationships, thus guiding the transition from natural language processing into a conceptual model. Such an ontology is not currently available because it would be very difficult and time consuming to produce. In this thesis, a semi-automated system for mapping natural language text into conceptual models is proposed. The model, which is called SACMES, combines a linguistic approach with an ontological approach and human intervention to achieve the task. The model learns from the natural language specifications that it processes, and stores the information that is learnt in a conceptual model ontology and a user history knowledge database. It then uses the stored information to improve performance and reduce the need for human intervention. The evaluation conducted on SACMES demonstrates that (1) designers’ creation of conceptual models is improved when using the system comparing with not using any system, and that (2) the performance of the system is improved by processing more natural language requirements, and thus, the need for human intervention has decreased. However, these advantages may be improved further through development of the learning and retrieval techniques used by the system.

Item Type: Thesis (Doctoral)
Subjects: T Technology > T Technology (General)
Schools: School of Computing and Engineering
Depositing User: Andrew Strike
Date Deposited: 04 Sep 2018 11:12
Last Modified: 16 May 2020 01:38
URI: http://eprints.hud.ac.uk/id/eprint/34665

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