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Updated: May 4, 2026

A Knowledge Graph Approach to Elucidate the Role of Organellar Pathways in Disease via Biomedical Reports

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Aprendizaje de Representación Contrastiva de Hipergrafía Aumentada con Conocimiento Semántico para la Clasificación

Ratri Mukherjee¹, Kishlay Jha¹

¹University of Iowa, Iowa City, Iowa, USA.

Advances in knowledge discovery and data mining : ... Pacific-Asia Conference, PAKDD ..., proceedings. Pacific-Asia Conference on Knowledge Discovery and Data Mining

|December 26, 2025

Resumen

Este resumen es generado por máquina.

Este estudio presenta un enfoque novedoso de hipergrafía para la clasificación de texto biomédico con cero disparos, mejorando cómo se etiquetan los artículos científicos con conceptos no vistos como nuevas enfermedades y fármacos.

Palabras clave:

clasificación de texto multietiqueta biomédica aprendizaje contrastivo hipergrafía semántica aprendizaje de cero disparos

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Área de la Ciencia:

Informática biomédica
Procesamiento del lenguaje natural
Aprendizaje automático

Sus antecedentes:

La clasificación de texto biomédico con cero disparos es crucial para etiquetar artículos científicos con conceptos novedosos.
Los métodos existentes luchan por capturar relaciones semánticas complejas entre entidades biomédicas.
Constantemente surgen nuevas enfermedades, genes y fármacos, lo que requiere sistemas de clasificación adaptables.

Objetivo del estudio:

Desarrollar un enfoque avanzado para la clasificación de texto biomédico con cero disparos.
Aprovechar eficazmente las relaciones semánticas de orden superior entre entidades biomédicas.
Mejorar el rendimiento de generalización en etiquetas no vistas en texto biomédico.

Principales métodos:

Se propuso un enfoque novedoso que utiliza una estructura de hipergrafía para modelar relaciones semánticas de orden superior.
Se introdujo una estrategia de aumento utilizando conocimiento del dominio biomédico para generar vistas de hipergrafía mejoradas.
Se desarrollaron representaciones de características robustas para entidades biomédicas.

Principales resultados:

El enfoque de hipergrafía propuesto mejoró significativamente el rendimiento de clasificación de cero disparos.
Las vistas de hipergrafía aumentadas mejoraron la capacidad del modelo para capturar información semántica compleja.
Los experimentos en un gran corpus biomédico validaron la efectividad del enfoque.

Conclusiones:

El método basado en hipergrafía ofrece una solución potente para la clasificación de texto biomédico con cero disparos.
Aprovechar el conocimiento semántico a través del aumento de hipergrafía conduce a una mejor generalización.
Este enfoque aborda el desafío de clasificar eficazmente los conceptos biomédicos emergentes.