Ensuring privacy is becoming one of the biggest challenges for digital service providers in the era of the Internet of Everything (IoE), where billions of interconnected devices generate vast amounts of sensitive data. Metadata is one of the fundamental building blocks for developing Privacy-Enhancing Technologies (PETs), which can assist with the assessment of privacy issues. The existence of metadata repositories facilitates the development of these tools. However, effective exploitation is not limited to querying these repositories. It is essential to ensure that the available data is of a high enough quality to be trusted for validating applications, detecting conflicts, and proposing solutions to these issues. This thesis presents an innovative approach to this challenge by combining ontologies with artificial intelligence techniques. To the best of our knowledge, this has not been explored in this context before. As a proof of concept, the approach will be applied to the App-PIMD repository, which contains metadata from over 13,000 mobile applications.