Farm animal health management systems (FAHMSs) face significant challenges in data acquisition, integration, and analysis. In this context, the semantics of agriculture data, which takes advantage of semantic web technologies, is an important tool for improving data management and enabling informed decision-making. However, existing systems lack standardization, integrity, interoperability, reusability, and advanced analytical reasoning. The authors propose an ontology-driven, knowledge-based framework for FAHMSs to address these challenges. Their framework focuses on a cattle application scenario and provides a standardized framework, a species-specific Livestock Health Ontology (LHO), Resource Descriptive Framework (RDF) data generation, and semantic interoperability. This research aims to improve disease surveillance and early detection, leading to better animal health outcomes. The chapter comprehensively analyzes the background knowledge, presents the methodology as a case study, and concludes with future research directions and challenges.