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January 23, 2020
Paper (Preprint, Research Paper, Review Paper, White Paper, etc.)
Title

Towards context in large scale biomedical knowledge graphs

Title Supplement
Published on arXiv
Abstract
Contextual information is widely considered for NLP and knowledge discovery in life sciences since it highly influences the exact meaning of natural language. The scientific challenge is not only to extract such context data, but also to store this data for further query and discovery approaches. Here, we propose a multiple step knowledge graph approach using labeled property graphs based on polyglot persistence systems to utilize context data for context mining, graph queries, knowledge discovery and extraction. We introduce the graph-theoretic foundation for a general context concept within semantic networks and show a proof-of-concept based on biomedical literature and text mining. Our test system contains a knowledge graph derived from the entirety of PubMed and SCAIView data and is enriched with text mining data and domain specific language data using BEL. Here, context is a more general concept than annotations. This dense graph has more than 71M nodes and 850M relationships. We discuss the impact of this novel approach with 27 real world use cases represented by graph queries.
Author(s)
Dörpinghaus, Jens
Fraunhofer-Institut für Algorithmen und Wissenschaftliches Rechnen SCAI  
Stefan, Andreas
Fraunhofer-Institut für Algorithmen und Wissenschaftliches Rechnen SCAI  
Schultz, Bruce  
Fraunhofer-Institut für Algorithmen und Wissenschaftliches Rechnen SCAI  
Jacobs, Marc  
Fraunhofer-Institut für Algorithmen und Wissenschaftliches Rechnen SCAI  
DOI
10.48550/arXiv.2001.08392
Language
English
Fraunhofer-Institut für Algorithmen und Wissenschaftliches Rechnen SCAI  
Keyword(s)
  • Databases

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