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A Knowledge Graph Approach to Elucidate the Role of Organellar Pathways in Disease via Biomedical Reports
07:35

A Knowledge Graph Approach to Elucidate the Role of Organellar Pathways in Disease via Biomedical Reports

Published on: October 13, 2023

Natural Language Processing methods and systems for biomedical ontology learning.

Kaihong Liu1, William R Hogan, Rebecca S Crowley

  • 1Department of Biomedical Informatics, University of Pittsburgh School of Medicine, PA 15232, USA.

Journal of Biomedical Informatics
|July 22, 2010
PubMed
Summary
This summary is machine-generated.

Developing biomedical ontologies is challenging due to manual efforts. This review explores automated methods using Natural Language Processing and machine learning to enrich ontologies from text, improving coverage and maintenance.

Related Experiment Videos

Last Updated: Jun 10, 2026

A Knowledge Graph Approach to Elucidate the Role of Organellar Pathways in Disease via Biomedical Reports
07:35

A Knowledge Graph Approach to Elucidate the Role of Organellar Pathways in Disease via Biomedical Reports

Published on: October 13, 2023

Area of Science:

  • Biomedical Informatics
  • Computational Biology
  • Knowledge Representation

Background:

  • Domain ontologies are crucial in biomedical informatics but face development challenges.
  • Manual ontology creation is time-consuming, error-prone, and resource-intensive.
  • Limited resources lead to incomplete coverage and difficulties in updating ontologies.

Purpose of the Study:

  • To review existing methodologies for automating ontology enrichment.
  • To discuss systems that leverage Natural Language Processing (NLP), information extraction, information retrieval, and machine learning.
  • To explore how these automated techniques can enhance biomedical ontology development.

Main Methods:

  • Literature review of NLP, information extraction, information retrieval, and machine learning techniques.
  • Analysis of existing systems for ontology enrichment.
  • Discussion of the application of these methods to biomedical ontologies.

Main Results:

  • Identified various automated techniques applicable to ontology enrichment.
  • Highlighted the potential of NLP and machine learning for improving ontology coverage.
  • Demonstrated the feasibility of using free-text documents for ontology development.

Conclusions:

  • Automated methods offer a viable solution to the challenges of biomedical ontology development.
  • NLP and machine learning can significantly improve the efficiency and quality of ontologies.
  • Further research can optimize these techniques for broader biomedical applications.