A novel link prediction approach on clinical knowledge graphs utilizing graph structures
Jens Dörpinghaus, Tobias Hübenthal, Jennifer Faber
Citation: Proceedings of the 17th Conference on Computer Science and Intelligence Systems, M. Ganzha, L. Maciaszek, M. Paprzycki, D. Ślęzak (eds). ACSIS, Vol. 30, pages 43–52 (2022)
Abstract. 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.
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