Designing and conceptualising ontology patterns for modelling cross-domain health information

The large amount and wide variety of data generated today require special tools and techniques for analysis and inferencing. This is especially evident in healthcare where data volume is growing at an exponential rate. The major contributors to this data influx are sources such as biomedical research and electronic patient records. With the increasing importance being placed on holistic care, it has become vital to analyse such disparate sources of information affecting patient health. Using the medical (systemic) and dental (oral health) domains as the application use case areas, this research investigates the challenges that exist in the way of development of cross-domain ontology (representation language, guiding framework, and inferencing concepts). The overreaching goal of this research is to provide a reusable development process for such cross-domain ontology that will subsequently aid in the development of intelligent decision support systems for holistic patient care.To that end, this research investigates the application of ontology design patterns in building a cross-domain systemic and oral health ontology that can be computationally exploited by reasoners for deriving new inferences and actionable knowledge. In general, ontology design patterns are employed to address recurring modelling problems during an ontology development process and to encourage knowledge reuse. The patterns also act as guiding templates since they explicitly encode the rationale behind the usage of the ontology representation language constructors and axioms. However, a major research gap identified in this thesis is the absence of reusable health patterns for building the cross-domain ontology. In trying to address the research gap, ten new patterns are proposed in this thesis. These patterns capture some of the common modelling requirements of a healthcare ontology in a formal manner so that they can be reused for other ontologies with similar requirements. The applicability of the proposed patterns in building an expressive and formal ontology is demonstrated by instantiating them with real-world use cases in a representative cross-domain ontology. Further, the proposed patterns and the ontology development process are validated using formalised competency questions and an example implementation of the ontology is done in Protege to evaluate its utility for reasoning and decision support over cross-domain patient information