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A Knowledge Graph Approach to Elucidate the Role of Organellar Pathways in Disease via Biomedical Reports
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Published on: October 13, 2023

Auto Ontology: Towards Automated Term-to-Concept Assignment in Microbiology Analytics.

Moritz Grob1,2, Leonhard Hauptfeld2, Julia Liepold2,3

  • 1Medical University of Vienna, Center for Medical Data Science, Institute of Artificial Intelligence, Spitalgasse 23, 1090 Vienna, Austria.

Studies in Health Technology and Informatics
|May 23, 2026
PubMed
Summary
This summary is machine-generated.

Text embedding models can help automate adding synonyms to clinical ontologies, improving efficiency. BioLORD-2023-M embeddings show promise for semi-automated concept mapping in ontology maintenance.

Keywords:
AutomationMomo – Monitoring of MicroorganismsNatural Language ProcessingOntology MaintenanceSemantic Search

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Cloud-Based Phrase Mining and Analysis of User-Defined Phrase-Category Association in Biomedical Publications
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Published on: February 23, 2019

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Last Updated: May 24, 2026

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Cloud-Based Phrase Mining and Analysis of User-Defined Phrase-Category Association in Biomedical Publications
09:20

Cloud-Based Phrase Mining and Analysis of User-Defined Phrase-Category Association in Biomedical Publications

Published on: February 23, 2019

Area of Science:

  • Medical Informatics
  • Computational Linguistics
  • Bioinformatics

Background:

  • Clinical ontology maintenance is crucial for data standardization.
  • Manual addition of synonyms from laboratory systems is labor-intensive.

Purpose of the Study:

  • To evaluate text embedding models for semi-automated synonym-to-concept mapping.
  • To assess the feasibility of using semantic similarity for ontology curation.

Main Methods:

  • Utilized the Momo microbiology ontology.
  • Indexed term embeddings locally using Qdrant.
  • Compared semantic similarity for assigning unseen terms to known concepts.
  • Evaluated performance using Top-k recall, Mean Reciprocal Rank, and F1-score.

Main Results:

  • Dense embeddings, specifically BioLORD-2023-M, achieved a Top-1 recall of 0.68 and an F1-score of 0.67.
  • The model demonstrated sufficient accuracy for human-validated workflows.
  • Fully automatic assignment is not yet feasible.

Conclusions:

  • Text embedding models offer a promising approach for semi-automated ontology maintenance.
  • Semantic retrieval can support practical ontology curation in resource-constrained clinical settings.