An efficient approach towards the generation and analysis of interoperable clinical data in a knowledge graph

Knowledge graphs have been shown to play an important role in recent knowledge mining settings, for example in the fields of life sciences or bioinformatics. Contextual information is widely used for NLP and knowledge discovery tasks, since it highly influences the exact meaning of expressions and also queries on data. The contributions of this paper are (1) an efficient approach towards interoperable data, (2) a runtime analysis of 14 real- world use cases represented by graph queries and (3) a unique view on clinical data and its application, combining methods of algorithmic optimisation, graph theory and data science.