Context mining and graph queries on giant biomedical knowledge graphs

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Dörpinghaus, JensFederal Institute for Vocational Education and Training (BIBB), Germany;German Center for Neurodegenerative Diseases (DZNE), Germany (författare)

Context mining and graph queries on giant biomedical knowledge graphs

Artikel/kapitelEngelska2022

Förlag, utgivningsår, omfång ...

2022-03-29
Springer,2022
electronicrdacarrier

Nummerbeteckningar

LIBRIS-ID:oai:DiVA.org:lnu-131820
https://urn.kb.se/resolve?urn=urn:nbn:se:lnu:diva-131820URI
https://doi.org/10.1007/s10115-022-01668-7DOI

Kompletterande språkuppgifter

Språk:engelska
Sammanfattning på:engelska

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Ämneskategori:ref swepub-contenttype
Ämneskategori:art swepub-publicationtype

Anmärkningar

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 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 Biological Expression Language. 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. 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.

Ämnesord och genrebeteckningar

NATURVETENSKAP Data- och informationsvetenskap Systemvetenskap, informationssystem och informatik hsv//swe
NATURAL SCIENCES Computer and Information Sciences Information Systems hsv//eng
Informatik
Information Systems

Biuppslag (personer, institutioner, konferenser, titlar ...)

Stefan, AndreasFraunhofer Institute for Algorithms and Scientific Computing SCAI, Germany;Bonn-Rhein-Sieg University of Applied Sciences, Germany (författare)
Schultz, BruceFraunhofer Institute for Algorithms and Scientific Computing SCAI, Germany (författare)
Jacobs, MarcFraunhofer Institute for Algorithms and Scientific Computing SCAI, Germany (författare)
Federal Institute for Vocational Education and Training (BIBB), Germany;German Center for Neurodegenerative Diseases (DZNE), GermanyFraunhofer Institute for Algorithms and Scientific Computing SCAI, Germany;Bonn-Rhein-Sieg University of Applied Sciences, Germany (creator_code:org_t)

Sammanhörande titlar

Ingår i:Knowledge and Information Systems: Springer64:5, s. 1239-12620219-13770219-3116

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Knowledge and Information Systems (Sök värdpublikationen i LIBRIS)

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Av författaren/redakt...: Dörpinghaus, Jen ...; Stefan, Andreas; Schultz, Bruce; Jacobs, Marc

Om ämnet

NATURVETENSKAP: NATURVETENSKAP; och Data och informa ...; och Systemvetenskap ...

Artiklar i publikationen: Knowledge and In ...

Av lärosätet: Linnéuniversitetet

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