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Semantic transference for enriching multilingual biomedical knowledge resources.

María Pérez1, Rafael Berlanga1

  • 1Department of Computer Languages and Systems, Universitat Jaume I, Avda. Vicent Sos Baynat s/n, 12071 Castellón, Spain.

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|September 20, 2015
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Summary
This summary is machine-generated.

This study enhances non-English biomedical knowledge resources (KRs) by aligning multilingual lexicons. Enriched Spanish, French, and German KRs significantly increased concept counts, improving biomedical data accessibility.

Keywords:
Multilingual biomedical knowledge resourcesSemantic transferenceTerm alignment

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Area of Science:

  • Biomedical Informatics
  • Computational Linguistics
  • Natural Language Processing

Background:

  • Biomedical knowledge resources (KRs) are predominantly in English, limiting applications in other languages.
  • A scarcity of non-English biomedical data hinders global research and development.
  • Existing multilingual KRs offer potential for cross-lingual knowledge expansion.

Purpose of the Study:

  • To enrich non-English biomedical knowledge resources by leveraging existing multilingual KRs.
  • To develop and combine automatic methods for generating accurate pair-wise language alignments.
  • To facilitate semantic transference between KRs across different languages.

Main Methods:

  • Utilized GIZA++ and Moses translation methods for language alignment.
  • Developed a novel ad hoc method specifically for multilingual KRs.
  • Ensured semantic coherence of generated alignments for reliable knowledge transfer.

Main Results:

  • Significantly expanded the Spanish UMLS Metathesaurus from 286,659 to 1,514,217 concepts.
  • Increased the French UMLS Metathesaurus from 83,119 to 1,104,968 concepts.
  • Grew the German UMLS Metathesaurus from 86,842 to 1,136,020 concepts.

Conclusions:

  • The proposed method effectively enriches non-English biomedical KRs using multilingual lexicons.
  • Automatic alignment and semantic transference enhance the scope and utility of biomedical knowledge.
  • This approach addresses the challenge of non-English data scarcity in biomedical applications.