Ontology Capture Based On Interpretive Structural Modeling

Ontologies provide structured knowledge representation but can be challenging to create. Ontologies require standardization, uniformity, and sharing, which is important for information systems and organizational management, where knowledge representation impacts decision-making. This article proposes the Ontology Capture method based on Interpretive Structural Modeling (OCT-ISM) as a methodology for capturing ontologies in any knowledge domain. This work discusses the limitations and solutions of using Interpretive Structural Modeling (ISM) to build an ontology, focusing on how it can enhance information systems by structuring relevant organizational data. With low computational cost, OCT-ISM can classify and hierarchize objects and structure static or dynamic ontologies useful in hierarchy, choice, or disambiguation situations, making it a practical tool for information systems management. The paper presents two main contributions: (i) the development of OCT-ISM, a simple and efficient method for building ontologies using ISM, and (ii) a demonstration of the use of this method in a domain within an organizational context as a use case, showcasing its applicability in improving knowledge management and decision support systems.