Knowledge graphs combine characteristics of several data management paradigms:

Database, because the data can be explored via structured queries; Graph, because they can be analyzed as any other network data structure; Knowledge base, because they bear formal semantics, which can be used to interpret the data and infer new facts.

The python wrapper of neo4j is used to create the knowledge graph with specified nodes & relationships.

The GraphQL queries returns the data demanded from the node or relationship

https://github.com/LasseRegin/medical-question-answer-data

https://github.com/liuhuanyong/QASystemOnMedicalKG/blob/master/data/medical.json https://www.kaggle.com/priya1207/diseases-dataset https://www.kaggle.com/usamag123/disease-prediction-through-symptoms

https://www.medicinenet.com/script/main/alphaidx.asp?p=a_dict

After running queries in graphQL on neo4j browser, the output shows all relationships and nodes as: