Application of Ontology in Data Governance and Knowledge Management in Artificial Intelligence-Based Organizations

Abstract

This study aimed to explain the application of ontology in improving data governance and knowledge management and to identify its implications for trustworthy intelligent systems, strategic decision-making, business innovation, and data-driven entrepreneurship in artificial intelligence-based organizations. This applied study employed a qualitative, exploratory design based on thematic analysis. The study population consisted of managers and specialists in artificial intelligence, data governance, knowledge management, data analytics, information architecture, digital transformation, and technology-based businesses. Participants were recruited through purposive sampling, and theoretical saturation was achieved after 12 semi-structured interviews. Following complete transcription, the data were analyzed using Braun and Clarke’s reflexive thematic analysis at the levels of basic, organizing, and global themes. MAXQDA software was used to organize and code the qualitative data. The trustworthiness of the findings was established through the criteria of credibility, transferability, dependability, and confirmability. The analysis generated 320 initial concepts. After removing repetitions and integrating semantically similar codes, 201 basic themes, 25 organizing themes, and five global themes were identified. The global themes comprised semantic data governance, ontology-based organization and flow of organizational knowledge, trustworthiness and accountability in intelligent systems, alignment of ontology with business strategy and data-driven entrepreneurship, and requirements for ontology implementation and maturity. The findings demonstrated that ontology supports the standardization of organizational concepts, semantic integration of heterogeneous data, improvement of data quality and traceability, and clarification of data ownership and responsibilities. It also facilitates knowledge sharing and retrieval, strengthens organizational memory, improves the explainability and auditability of algorithmic decisions, and contributes to the identification and reduction of algorithmic bias. Furthermore, ontology connects organizational data and knowledge with managerial decisions, innovation opportunities, knowledge-based products, and value creation. Ontology constitutes a strategic semantic infrastructure for integrating data, knowledge, artificial intelligence, and business objectives. Its successful implementation requires an integrated data architecture, a knowledge-sharing culture, interdisciplinary expertise, senior management support, effective change governance, and the continuous maintenance and updating of conceptual models.