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Related Experiment Video

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Cloud-Based Phrase Mining and Analysis of User-Defined Phrase-Category Association in Biomedical Publications
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Improving accessibility and distinction between negative results in biomedical relation extraction.

Diana Sousa1, Andre Lamurias1, Francisco M Couto1

  • 1LASIGE, Faculdade de Ciências, Universidade de Lisboa, Campo Grande 1749-016, Portugal.

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|July 8, 2020
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Summary

Distinguishing false, negative, and unknown relations in biomedical data is crucial. This study introduces methods to automatically differentiate these relation types, improving research efficiency and preventing redundant studies.

Keywords:
biomedical researchknowledge basenegative resultsrelation extraction

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Area of Science:

  • Biomedical Informatics
  • Natural Language Processing
  • Knowledge Representation

Background:

  • Accessible negative results are vital for researchers and clinicians to avoid redundant research and limit search spaces.
  • Existing biomedical relation extraction datasets often fail to differentiate between false and negative relations.
  • Distant supervision techniques can introduce false negative relations, representing undocumented or unknown relationships.

Purpose of the Study:

  • To enhance the distinction between false (F), negative (N), and unknown (U) relations in biomedical datasets.
  • To develop methods for the automatic classification of FNU relations, specifically within the phenotype-gene relations corpus.

Main Methods:

  • Manual revision of a subset of relations previously marked as false in the phenotype-gene relations corpus.
  • Development of a system for the automatic distinction between false, negative, and unknown relations.
  • Annotation of a sample of 127 phenotype-gene relations as FNU.

Main Results:

  • A manually annotated dataset of 127 FNU relations was created.
  • An automatic distinction method achieved a weighted-F1 score of 0.5609 for classifying FNU relations.
  • The study provides initial steps towards automated FNU relation identification.

Conclusions:

  • Accurate differentiation of false, negative, and unknown relations is essential for biomedical knowledge discovery.
  • The proposed approach offers a foundation for improving the quality and completeness of biomedical knowledge bases.
  • Further development is needed to enhance the performance of automatic FNU relation distinction.