This thesis presents the concept of hybrid semantic-document models to aid information management when using standards for complex technical domains such as military data communication. These standards are traditionally text based documents for human interpretation, but prose sections can often be ambiguous and can lead to discrepancies and subsequent implementation problems. Many organisations produce semantic representations of the material to ensure common understanding and to exploit computer aided development. In developing these semantic representations, no relationship is maintained to the original prose. Maintaining relationships between the original prose and the semantic model has key benefits, including assessing conformance at a semantic level, and enabling original content authors to explicitly define their intentions, thus reducing ambiguity and facilitating computer aided functionality. Through the use of a case study method based on the military standard MIL-STD-6016C, a framework of relationships is proposed. These relationships can integrate with common document modelling techniques and provide the necessary functionality to allow semantic content to be mapped into document views. These relationships are then generalised for applicability to a wider context. Additionally, this framework is coupled with a templating approach which, for repeating sections, can improve consistency and further enhance quality. A reflective approach to model driven web rendering is presented and evaluated. This reflective approach uses self-inspection at runtime to read directly from the model, thus eliminating the need for any generative processes which result in data duplication across source used for different purpose.
| Identifer | oai:union.ndltd.org:bl.uk/oai:ethos.bl.uk:763412 |
| Date | January 2013 |
| Creators | Clowes, Darren |
| Publisher | Loughborough University |
| Source Sets | Ethos UK |
| Detected Language | English |
| Type | Electronic Thesis or Dissertation |
| Source | https://dspace.lboro.ac.uk/2134/14736 |
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