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Context mining and graph queries on giant biomedical knowledge graphs

  • 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.

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Document Type:Article
Author:Jens Dörpinghaus, Andreas Stefan, Bruce Schultz, Marc Jacobs
Parent Title (English):Knowledge and Information Systems
Number of pages:24
First Page:1239
Last Page:1262
Publishing Institution:Hochschule Bonn-Rhein-Sieg
Date of first publication:2022/03/29
Copyright:© The Author(s) 2022
Funding:This study was funded by Fraunhofer Society under the MAVO Project; Human Brain Pharmacome; EU/EFPIA Innovative Medicines Initiative Joint Undertaking under AETIONOMY (115568 to D.D.F.); European Union’s Seventh Framework Programme (FP7/2007-2013); and EFPIA.
Keyword:Complexity; Current research information systems; Graph embeddings; Graph theory; Knowledge Graphs; NLP; Semantic search
Departments, institutes and facilities:Fachbereich Informatik
Dewey Decimal Classification (DDC):0 Informatik, Informationswissenschaft, allgemeine Werke / 00 Informatik, Wissen, Systeme / 006 Spezielle Computerverfahren
Entry in this database:2022/04/01
Licence (German):License LogoCreative Commons - CC BY - Namensnennung 4.0 International