Browser fingerprinting poses a significant privacy and security threat by covertly collecting unique user attributes such as device information, screen resolution, and installed fonts, enabling persistent tracking without relying on cookies. This paper introduces a novel hybrid detection method that combines JavaScript function interception and network request monitoring to collect and analyse fingerprinting scripts across the top 1,000 TRANCO websites, categorising them into widespread baseline, niche and highly aggressive scripts based on their attribute collection behaviour. Furthermore, we present our work towards the development of an ontology for browser fingerprinting detection, enabling structured reasoning about tracking techniques, relationships between websites and attributes, and classification of fingerprinting severity. The proposed ontology can be extended and integrated with future knowledge graphs to facilitate automated detection and reasoning about invasive tracking practices.