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2 Annals of Computer Science and Information Systems

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A novel link prediction approach on clinical knowledge graphs utilizing graph structures

Dörpinghaus Jens, Hübenthal Tobias, Faber Jennifer

Annals of Computer Science and Information Systems

2022

Vol. 30

43--52

This paper presents a novel approach towards link prediction in clinical knowledge graphs. They play a central role for 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 build 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 patient-nodes usually common methods for link prediction and graph embeddings are problematic and thus we will (3) present a novel approach for link prediction utilizing 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.

An Efficient Approach Towards the Generation and Analysis of Interoperable Clinical Data in a Knowledge Graph

Dörpinghaus Jens, Weil Vera, Schaaf Sebastian, Hübenthal Tobias

Annals of Computer Science and Information Systems

2021

Vol. 25

59--68

Knowledge graphs have been shown to play an important role in recent knowledge mining and discovery, for example in the field of life sciences or bioinformatics. Contextual information is widely used for NLP and knowledge discovery in life sciences since it highly influences the exact meaning of natural language and also queries for 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.