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Updated: May 22, 2025

A Knowledge Graph Approach to Elucidate the Role of Organellar Pathways in Disease via Biomedical Reports
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From biomedical knowledge graph construction to semantic querying: a comprehensive approach.

Ling Wang1, Haoyu Hao1, Xue Yan1

  • 1School of Computer Science, Northeast Electric Power University, 169 Changchun Street, Jilin, 132012, China.

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Summary
This summary is machine-generated.

This study introduces a new approach for building biomedical knowledge graphs and querying them. The BioPLBC model enhances text annotation, and the ALEQ algorithm speeds up semantic queries, improving accuracy.

Keywords:
BioNERData miningNatural language processingSemantic query

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Area of Science:

  • Biomedical Informatics
  • Knowledge Representation
  • Natural Language Processing

Background:

  • Effective integration and management of complex biomedical information are crucial.
  • Knowledge graphs offer a powerful solution for organizing and accessing medical data.
  • Existing methods for biomedical knowledge graph construction and querying require enhancement.

Purpose of the Study:

  • To present a comprehensive approach for biomedical knowledge graph construction and semantic querying.
  • To introduce the BioPLBC model for accurate entity annotation in medical texts.
  • To develop the ALEQ algorithm for efficient and accurate knowledge graph querying.

Main Methods:

  • Proposed the BioPLBC model, integrating BioBERT, part of speech, and lexical features for medical entity annotation.
  • Developed the Adaptive Locating and Expanding Query (ALEQ) algorithm for dynamic subregion expansion in knowledge graph queries.
  • Utilized a constructed biomedical knowledge graph for algorithm testing and validation.

Main Results:

  • The BioPLBC model demonstrated superior accuracy in entity annotation compared to baseline models across various datasets.
  • The ALEQ algorithm significantly improved query accuracy and speed.
  • The whole-process approach provides a robust framework for biomedical knowledge management.

Conclusions:

  • The BioPLBC model and ALEQ algorithm offer significant advancements in biomedical knowledge graph construction and semantic querying.
  • This research facilitates more efficient and accurate access to complex medical information.
  • The developed methods have strong potential for applications in clinical decision support and biomedical research.