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Mejora del conocimiento y utilización completa de la información de documentos para la extracción de relaciones

Xin Chen1, Liang He2,3, Yuhan Liu1

  • 1School of Computer Science and Technology, Xinjiang University, Urumqi, 830017, China.

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|December 20, 2025
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Resumen

Este estudio presenta KnowFDI, un marco novedoso para la extracción de relaciones biomédicas a nivel de documento. KnowFDI mejora la precisión integrando el contexto local/global, la estructura del documento y el conocimiento externo para un mejor descubrimiento del conocimiento biomédico.

Palabras clave:
Minería de textos biomédicosInformación contextualExtracción de relaciones a nivel de documentoRed neuronal gráficaMejora del conocimiento

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Área de la Ciencia:

  • Informática Biomédica
  • Procesamiento del Lenguaje Natural
  • Biología Computacional

Sus antecedentes:

  • La extracción de relaciones biomédicas a nivel de documento (BioDocuRE) es crucial para descubrir relaciones que abarcan varias oraciones.
  • Los métodos existentes tienen dificultades para integrar el conocimiento externo y explotar la estructura del documento para relaciones complejas.

Objetivo del estudio:

  • Presentar KnowFDI, un marco novedoso para BioDocuRE a nivel de documento.
  • Mejorar la precisión de la extracción de relaciones biomédicas complejas y de largo alcance.

Principales métodos:

  • KnowFDI combina el contexto local/global, la estructura del documento (relaciones entre oraciones a través de nodos puente) y el conocimiento externo centrado en la entidad.
  • Utiliza un modelo de lenguaje preentrenado para la codificación y el aprendizaje de representación multivista con fusión de atención por canales.
  • Incorpora conocimiento descriptivo de bases de conocimiento biomédicas externas para enriquecer la semántica de las entidades.

Principales resultados:

  • KnowFDI logra un rendimiento de última generación en los puntos de referencia CDR y GDA.
  • Demuestra mejoras significativas en la extracción de relaciones general y entre oraciones.
  • Supera a los métodos anteriores en la identificación de relaciones biomédicas complejas y de largo alcance.

Conclusiones:

  • La fusión jerárquica de información y la integración de conocimiento externo de KnowFDI son cruciales para descifrar dependencias complejas a nivel de documento.
  • El marco mejora eficazmente el descubrimiento de conocimiento biomédico a través de una mejor extracción de relaciones.