This paper presents a novel approach towards link prediction in clinical knowledge graphs.They play a central role in linking data from different data sources and are widely used in big data integration, especially for connecting data from different domains.We present a knowledge graph initially built on data from a clinical trial on Spinocerebellar ataxia type 3 (SCA3), which is a rare autosomal dominant inherited disorder.The contributions of this paper are (1) to create a feasible data representation schema capable of handling clinical imaging data in a knowledge graph and to ( 2) convert the data efficiently into a knowledge graph.Due to the limited amount of patientnodes usually common methods for link prediction and graph embeddings are problematic and thus we will (3) present a novel approach for link prediction utilising graph structures and Conditional Random Fields.In addition, we present (4) an extensive evaluation underlining the importance of (a) data management and (b) further research on link prediction using graph structures.

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 realworld 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.