| Summary: | Ontologies are widely used in life sciences to model scienti c knowledge. The engineering of these ontologies is well-studied and there are a variety of methodologies and techniques, some of which have been re-purposed from software engineering methodologies and techniques. However, due to the complex nature of bio-ontologies, they are not resistant to errors and mistakes. This is especially true for more expressive and/or larger ontologies. In order to improve on this issue, we explore a variety of software engineering techniques that were re-purposed in order to aid ontology engineering. This exploration is driven by the construction of two light-weight ontologies, The Mitochondrial Disease Ontology and The Karyotype Ontology. These ontologies have speci c and useful computational goals, as well as providing exemplars for our methodology. This thesis discusses the modelling decisions undertaken as well as the overall success of each ontological model. Due to the added knowledge capture steps required for the mitochondrial knowledge, The Karyotype Ontology is further developed than The Mitochondrial Disease Ontology. Speci cally, this thesis explores the use of a pattern-driven and programmatic approach to bio-medical ontology engineering. During the engineering of our biomedical ontologies, we found many of the components of each model were similar in logical and textual de nitions. This was especially true for The Karyotype Ontology. In software engineering a common technique to avoid replication is to abstract through the use of patterns. Therefore we utilised localised patterns to model these highly repetitive models. There are a variety of possible tools for the encoding of these patterns, but we found ontology development using Graphical User Interface (GUI) tools to be time-consuming due to the necessity of manual GUI interaction when the ontology needed updating. With the development of Tawny- OWL, a programmatic tool for ontology construction, we are able to overcome this issue, with the added bene t of using a single syntax to express both simple and - i - patternised parts of the ontology. Lastly, we brie y discuss how other methodologies and tools from software engineering, namely unit tests, di ng, version control and Continuous Integration (CI) were re-purposed and how they aided the engineering of our two domain ontologies. Together, this knowledge increases our understanding in ontology engineering techniques. By re-purposing software engineering methodologies, we have aided construction, quality and maintainability of two novel ontologies, and have demonstrated their applicability more generally.
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