Today a wealth of knowledge and data are distributed using Semantic Web standards. Especially in the (bio)medical domain several sources like the SNOMED CT, NCI, MedDRA, MeSH, ICD-10 ontologies, and many more are distributed in RDF and OWL. These can be aligned and integrated in order to create one large medical Knowledge Base. However, integrating different and largely heterogeneous sources is far from trivial. First, although distributed in OWL many of the ontologies may not strictly follow the semantics of subClassOf as originally intended for faceted search or use as thesauri. Second, even when they do follow strict ontological guidelines, different ontologies may conceptualise the same domain in radically different ways. Analysing and understanding these sources before integrating them is highly beneficial. Third, monitoring and understanding how the structure of the Knowledge Base changes (evolves) after the integration is also crucial since changes to its structure may affect applications that are built on top of it. In the current paper we report on our Knowledge Base construction pipeline which is based on ontology integration. We focus on the various metrics, techniques, and tools we have developed in order to assist in achieving this large-scale integration task. Our work was motivated by the need for a medical Knowledge Base to be used to support digital healthcare services developed at Babylon Health. We present results on the metrics used to analyse various sources and the results of running the pipeline on several medical ontologies.