ONTOLOGY MERGING USING SEMANTICALLY-DEFINED MERGE CRITERIA AND OWL REASONING SERVICES: TOWARDS EXECUTION-TIME MERGING OF MULTIPLE CLINICAL WORKFLOWS TO HANDLE COMORBIDITIES
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Semantic web based decision support systems represent domain knowledge using ontologies that capture the domain concepts, their relationships and instances. Typically, decision support systems use a single knowledge model—i.e. a single ontology—which at times restricts the knowledge coverage to only select aspects of the domain knowledge. The integration of multiple knowledge models—i.e. multiple ontologies—provides a holistic knowledge model that encompasses multiple perspectives, orientations and instances. The challenge is the execution-time merging of multiple ontologies whilst maintaining knowledge consistency and procedural validity. Knowledge morphing aims at the intelligent merging of multiple computerized knowledge artifacts—represented as distinct ontological models—in order to create a holistic and networked knowledge model. In our research, we have investigated and developed a knowledge morphing framework—termed as OntoMorph—that supports ontology merging through: (1) Ontology Reconciliation whereby we harmonize multiple ontologies in terms of their vocabularies, knowledge coverage, and description granularities; (2) Ontology Merging where multiple reconciled ontologies are merged into a single merged ontology. To achieve ontology merging, we have formalized a set of semantically-defined merging criteria that determine ontology merge points, and describe the associated process-specific and knowledge consistency constraints that need to be satisfied to ensure consistent ontology merging; and (3) Ontology Execution whereby we have developed logic-based execution engines for both execution-time ontology merging and the execution of the merged ontology to infer knowledge-based recommendations. We have utilized OWL reasoning services, for efficient and decidable reasoning, to execute an OWL ontology. We have applied the OntoMorph framework for clinical decision support, more specifically to achieve the dynamic merging of multiple clinical practice guidelines in order to handle comorbid situations where a patient may have multiple diseases and hence multiple clinical guidelines are to be simultaneously operationalized. We have demonstrated the execution time merging of ontologically-modelled clinical guidelines, such that the decision support recommendations are derived from multiple, yet merged, clinical guidelines such that the inferred recommendations are clinically consistent. The thesis contributes new methods for ontology reconciliation, merging and execution, and presents a solution for execution-time merging of multiple clinical guidelines.