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Title: Reverse engineering domain ontologies to conceptual data models
Author: El-Ghalayini, Haya Ahmed.
ISNI:       0000 0001 3442 7852
Awarding Body: University of the West of England, Bristol,
Current Institution: University of the West of England, Bristol
Date of Award: 2007
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The nature of information systems is changing as they become more sophisticated and increasingly address more complex application domains. This has in turn complicates the representation of the functional requirements of a particular problem domain in terms of its basic entities and their relationships, as expressed in conceptual data models. Conceptual data models aim at establishing a link between user and domain requirements. The process of developing conceptual data models seems to be quite straightforward; however, it is often a lengthy and iterative process, and the output models can have a significant impact on the quality of the final system. As ontologies can capture consensual commitment about domain knowledge, the goal of this thesis is to study the extent to which consensual knowledge about a certain domain that has been captured in domain ontologies can participate in developing conceptual data models. Therefore, this thesis research introduces a framework in a form of so-called Transformation-Engine to generate a possible conceptual data model from a given domain ontology. The functionality of the Transformation-Engine constitutes of two main activities: (1) generating a suggested conceptual data model from a given domain ontology using a novel set of mapping rules between an ontology language and a conceptual data modelling constructs; (2) improving the quality of the generated conceptual data model elements using the newly introduced ontological quality graph. The results of this thesis show that conceptual data models that are developed from domain ontologies are comparable to the models that are traditionally developed during the elicitation stage. The approach does not generate a comprehensive conceptual data model automatically, but suggests relevant alternatives to modellers as they capture the basic entities and their relations for the problem domain identified by a domain community. The reverse-engineered conceptual data models aid understanding and communication, and facilitate eventual system integration.
Supervisor: Not available Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID:  DOI: Not available