Methodology for Multi-aspect Ontology Development: Use Case of DSS Based on Human-Machine Collective Intelligence

Abstract

The lack of interoperability observed in modern DSSs becomes even greater when complex systems covering multiple domains are considered. In the present research, the apparatus of multi-aspect ontologies is used as a means to represent knowledge of DSSs based on human-machine collective intelligence for enabling interoperability between the system components and coordinate interrelated processes. The available ontology development methodologies are not quite suitable for the development of multi-aspect ontologies because they leave aside the problem of choosing approaches for integration of reusable ontologies. Since the structure of a multi-aspect ontology imposes some restrictions on the aspects integration, the objective of this research is to propose a methodology for the development of multi-aspect ontologies that incorporates an aspects integration approach. For the research purpose, the existing ontology development methodologies have been analyzed and an ontology development pattern followed by most methodologies has been revealed. The developed four-stage methodology extends it with an aspects integration approach. The methodology is illustrated through the development of a multi-aspect ontology to support semantic interoperability in DSSs based on human-machine collective intelligence.