There are between 6,000 - 7,000 known rare diseases today. Identifying and diagnosing a patient with rare disease is time consuming, cumbersome, cost intensive and requires resources generally available only at large hospital centers. Furthermore, most medical doctors, especially general practitioners, will likely only see one patient with a rare disease if at all. A cognitive assistant for differential diagnosis in rare disease will provide the knowledge on all rare diseases online, help create a list of weighted diagnosis and access to the evidence base on which the list was created. The system is built on knowledge graph technology that incorporates data from ICD-10, DOID, medDRA, PubMed, Wikipedia, Orphanet, the CDC and anonymized patient data. The final knowledge graph comprised over 500,000 nodes. The solution was tested with 101 published cases for rare disease. The learning system improves over training sprints and delivers 79.5 % accuracy in finding the diagnosis in the top 1 % of nodes. A further learning step was taken to rank the correct result in the TOP 15 hits. With a reduced data pool, 51% of the 101 cases were tested delivering the correct result in the TOP 3 - 13 (TOP 6 on average) for 74% of these cases. The results show that data curation is among the most critical aspects to deliver accurate results. The knowledge graph technology demonstrates its power to deliver cognitive solutions for differential diagnosis in rare disease that can be applied in clinical practice.