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BioKGrapher: Initial evaluation of automated knowledge graph construction from biomedical literature

Fast facts

  • Further publishers

    Ahmad Idrissi-Yaghir, Kamyar Arzideh, Tabea Margareta Grace Pakull, Cynthia Sabrina Schmidt, Mikel Bahn, Georg Lodde, Elisabeth Livingstone, Dirk Schadendorf, Felix Nensa, Peter A. Horn

  • Publishment

    • 2024
  • Journal

    Computational and Structural Biotechnology Journal

  • Organizational unit

  • Subjects

    • Applied computer science
    • Medical Informatics (only for physicians)
  • Research fields

    • Medical Informatics (MI)
  • Publication format

    Journal article (Article)

Content

Background The growth of biomedical literature presents challenges in extracting and structuring knowledge. Knowledge Graphs (KGs) offer a solution by representing relationships between biomedical entities. However, manual construction of KGs is labor-intensive and time-consuming, highlighting the need for automated methods. This work introduces BioKGrapher, a tool for automatic KG construction using large-scale publication data, with a focus on biomedical concepts related to specific medical conditions. BioKGrapher allows researchers to construct KGs from PubMed IDs. Methods The BioKGrapher pipeline begins with Named Entity Recognition and Linking (NER+NEL) to extract and normalize biomedical concepts from PubMed, mapping them to the Unified Medical Language System (UMLS). Extracted concepts are weighted and re-ranked using Kullback-Leibler divergence and local frequency balancing. These concepts are then integrated into hierarchical KGs, with relationships formed using terminologies like SNOMED CT and NCIt. Downstream applications include multi-label document classification using Adapter-infused Transformer models. Results BioKGrapher effectively aligns generated concepts with clinical practice guidelines from the German Guideline Program in Oncology (GGPO), achieving F1 -Scores of up to 0.6. In multi-label classification, Adapter-infused models using a BioKGrapher cancer-specific KG improved micro F1 -Scores by up to 0.89 percentage points over a non-specific KG and 2.16 points over base models across three BERT variants. The drug-disease extraction case study identified indications for Nivolumab and Rituximab. Conclusion BioKGrapher is a tool for automatic KG construction, aligning with the GGPO and enhancing downstream task performance. It offers a scalable solution for managing biomedical knowledge, with potential applications in literature recommendation, decision support, and drug repurposing.

Notes and references

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