2019/12/08 by Jens Dörpinghaus, Alexander Apke, Dörpinghaus, Jens +5
Biochemistry, Genetics and Molecular Biology · Computer Science · #Biomedical Text Mining and Ontologies #Semantic Web and Ontologies #Bioinformatics and Genomic Networks
paper · pdf · doi:10.48550/arxiv.1912.06194
Here we present a holistic approach for data exploration on dense knowledge\ngraphs as a novel approach with a proof-of-concept in biomedical research.\nKnowledge graphs are increasingly becoming a vital factor in knowledge mining\nand discovery as they connect data using technologies from the semantic web. In\nthis paper we extend a basic knowledge graph extracted from biomedical\nliterature by context data like named entities and relations obtained by text\nmining and other linked data sources like ontologies and databases. We will\npresent an overview about this novel network. The aim of this work was to\nextend this current knowledge with approaches from graph theory. This method\nwill build the foundation for quality control, validation of hypothesis,\ndetection of missing data and time series analysis of biomedical knowledge in\ngeneral. In this context we tried to apply multiple-valued decision diagrams to\nthese questions. In addition this knowledge representation of linked data can\nbe used as FAIR approach to answer semantic questions. This paper sheds new\nlights on dense and very large knowledge graphs and the importance of a\ngraph-theoretic understanding of these networks.\n