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Semi-Supervised Learning to Identify UMLS Semantic Relations.

Yuan Luo1, Ozlem Uzuner2

  • 1Massachueets Institute of Technology.

AMIA Joint Summits on Translational Science Proceedings. AMIA Joint Summits on Translational Science
|May 9, 2015
PubMed
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This study introduces a semi-supervised method to automatically identify Unified Medical Language System (UMLS) semantic relations from PubMed text. The approach achieves over 70% F-measure for top-level relation clustering.

Area of Science:

  • Biomedical Informatics
  • Natural Language Processing
  • Knowledge Representation

Background:

  • The Unified Medical Language System (UMLS) Semantic Network requires manual updates by experts.
  • Automating the identification of UMLS semantic relations is crucial for efficient knowledge base maintenance.

Purpose of the Study:

  • To develop and implement a semi-supervised approach for automatically identifying UMLS semantic relations from biomedical narrative text.
  • To evaluate the performance of this automated method against a manually annotated corpus.

Main Methods:

  • A semi-supervised method was employed to analyze PubMed narrative text for semantic entity pairs.
  • Extracted semantic, syntactic, and orthographic features were used with seeded k-means clustering.
  • A ground truth corpus was created and annotated for the top two levels of the UMLS semantic relation hierarchy.

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Main Results:

  • The system achieved macro-averaged F-measures above 70% for top-level (2-way) UMLS relation clustering.
  • Macro-averaged F-measures exceeded 50% for second-level (7-way) UMLS relation clustering.
  • KL divergence was identified as the best-performing distance metric.

Conclusions:

  • The proposed semi-supervised approach effectively automates the identification of UMLS semantic relations from biomedical text.
  • This method offers a scalable alternative to manual expert review for updating the UMLS Semantic Network.
  • The system demonstrates promising performance, particularly for higher-level relation categorization.