The deliverable Ontology and Federated Execution Methods marks the achievement of Milestone 5 in the project, focusing on two critical areas: semantic ontology modeling for neurological diseases (Amyotrophic Lateral Sclerosis (ALS) and Multiple Sclerosis (MS)) and the design of federated execution methods. It synthesizes WP2, WP3, and WP4 contributions to provide a comprehensive framework for integrating clinical and genomic data through advanced ontology design and scalablequery execution frameworks. At its core, the Hereditary Ontology (HERO), central to this deliverable, ensures structured and interoperable representation of phenoclinical and genomic aspects of ALS and MS. Developed with adherence to the FAIR principles, it enables seamless integration with established standards, such as the Simple Knowledge Organization System (SKOS), and lays the foundation for extensibility to other biomedical domains. HERO’s modular structure, divided into clinical and genomic components, ensures clarity and scalability, while its hierarchical design captures the intricate relationships among symptoms, diagnostics, and genetic markers. Through iterative testing and real-world validation, HERO aligns closely with the domain’s requirements, ensuring accurate representation and utility in the context of neurological diseases. Hereditary federated execution environment enables robust analytics across clinical and genomic datasets. Two principal layers define the framework’s architecture. The Data Federation and Virtualization Layer unifies disparate data sources into a virtualized model, ensuring efficient and scalable data integration. Secondly, the Ontology-Based Data Access (OBDA) Layer leverages semantic ontology-driven query rewriting and unfolding techniques to enable federated query execution. These layers work in tandem to bridge heterogeneity in data representation, fostering interoperability and enhancing federated analytic workflows. The deliverable includes two primary use cases to validate the integrated framework. The first focuses on ALS data, demonstrating the system’s ability to derive clinical insights from distributed datasets related to patient symptoms and treatment outcomes. The second explores genomics queries, underscoring the framework’s capacity to handle distributed genomic data.