Assessing Knowledge Provenance Quality for CEO Decision Support: An OWL-DL Ontological Framework

Abstract

The ontology demonstrates approaches to enhancing the precision of knowledge provenance and outlines the systematic engineering of a knowledge provenance ontology designed to improve its reliability for executive decision-making. Utilizing architectural principles, we propose a formal model that evaluates knowledge provenance quality based on standardized coding systems such as PROV-O. The methodology follows a rapid prototyping lifecycle, employing the "Ontology 101" development process to transform domain expertise into a rigorous OWL-DL framework. By implementing constraints and functional properties, the ontology ensures data integrity and consistency, which are critical for CEOs navigating complex information environments. The results demonstrate that formal ontological reasoning, supported by tools such as Pellet, can effectively filter low-quality knowledge sources, providing a trustworthy basis for strategic corporate oversight.