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Biomedical Knowledge Graphs: Context, Queries and Complexity

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Computational Life Sciences

Part of the book series: Studies in Big Data ((SBD,volume 112))

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Abstract

Ontology-based mappings in knowledge graphs are a widely discussed topic in biomedical research. 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. Classical approaches use RDF triple stores, which have serious limitations. Here, 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 as 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. 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. Storing and querying a giant knowledge graph as a labeled property graph is still a technological challenge. Here we demonstrate how our data model is able to support the understanding and interpretation of biomedical data. We present several real world use cases that utilize our massive, generated knowledge graph derived from PubMed data and enriched with additional contextual data. Finally, we show a working example in context of biologically relevant information using SCAIView.

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Notes

  1. 1.

    https://www.ncbi.nlm.nih.gov/pubmed.

  2. 2.

    https://www.scaiview.com/.

  3. 3.

    https://neo4j.com/.

  4. 4.

    http://jsongraphformat.info/.

  5. 5.

    http://spring.io/projects/spring-boot.

  6. 6.

    https://spring.io/projects/spring-data.

  7. 7.

    https://neo4j.com/.

  8. 8.

    See https://www.pharmgkb.org/.

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Author information

Authors and Affiliations

  1. Federal Institute for Vocational Education and Training (BIBB), Bonn, Germany

    Jens Dörpinghaus

  2. German Center for Neurodegenerative Diseases (DZNE), Bonn, Germany

    Jens Dörpinghaus

  3. Mathematical Institute, University Koblenz-Landau, Koblenz, Germany

    Carsten Düing

  4. Department of Bioinformatics, Fraunhofer Institute for Algorithms and Scientific Computing SCAI, Schloss Birlinghoven, 53754, Sankt Augustin, Germany

    Andreas Stefan

  5. Bonn-Rhein-Sieg University of Applied Sciences, Sankt Augustin, Germany

    Andreas Stefan

Authors
  1. Jens Dörpinghaus
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  2. Carsten Düing
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  3. Andreas Stefan
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Corresponding author

Correspondence to Jens Dörpinghaus .

Editor information

Editors and Affiliations

  1. German Center for Neurodegenerative Diseases (DZNE), Bonn, Germany

    Jens Dörpinghaus

  2. Department for Mathematics and Computer Science, University of Cologne, Cologne, Germany

    Vera Weil

  3. German Center for Neurodegenerative Diseases (DZNE), Bonn, Germany

    Sebastian Schaaf

  4. High Performance Computing, Fraunhofer Institute for Algorithms and Scientific Computing SCAI, Sankt Augustin, Germany

    Alexander Apke

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Dörpinghaus, J., Düing, C., Stefan, A. (2022). Biomedical Knowledge Graphs: Context, Queries and Complexity. In: Dörpinghaus, J., Weil, V., Schaaf, S., Apke, A. (eds) Computational Life Sciences. Studies in Big Data, vol 112. Springer, Cham. https://doi.org/10.1007/978-3-031-08411-9_20

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