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Incrustación de conocimiento contrastivo con muestreo auto-ponderado discriminatorio

Sheng Wan¹, Yibing Zhan², Shirui Pan³

¹College of Artificial Intelligence, Nanjing Agricultural University, Nanjing, 211800, Jiangsu, China.

Neural networks : the official journal of the International Neural Network Society

|February 25, 2026

Resumen

Este resumen es generado por máquina.

El aprendizaje contrastivo (CL) mejora las incrustaciones de grafos de conocimiento (KG) ponderando adaptativamente las muestras negativas. Este marco de muestreo auto-ponderado discriminatorio (CoDiSS) mejora los modelos de incrustación de KG al centrarse en negativos informativos.

Palabras clave:

aprendizaje contrastivo de grafos aprendizaje de representación aprendizaje autosupervisado

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Área de la Ciencia:

Inteligencia Artificial
Aprendizaje Automático
Ciencia de Datos

Sus antecedentes:

La incrustación de grafos de conocimiento (KG) mapea los componentes de KG a espacios de baja dimensión.
Los modelos existentes de incrustación de KG se centran en las funciones de puntuación, descuidando los marcos de aprendizaje.
El aprendizaje contrastivo (CL) ofrece potencial para el aprendizaje de representación en incrustaciones de KG.

Objetivo del estudio:

Introducir un nuevo marco CL para la incrustación de KG que aborda la ineficiencia del muestreo negativo tradicional.
Mejorar la expresividad y el rendimiento de los modelos de incrustación de KG.

Principales métodos:

Desarrolló un marco CL flexible denominado "Incrustación de conocimiento contrastivo con muestreo auto-ponderado discriminatorio" (CoDiSS).
Implementó un mecanismo de ponderación adaptativa para tripletes negativos basado en su contribución al aprendizaje.
Introdujo una pérdida de refinamiento de ponderación discriminatoria (DWR) para remodelar las distribuciones de puntuación negativa.

Principales resultados:

CoDiSS asigna adaptativamente importancia a los tripletes negativos, a diferencia del muestreo uniforme.
La pérdida DWR separa eficazmente los negativos informativos de los falsos.
CoDiSS mejora el rendimiento de varios modelos de incrustación de KG (TransE, ComplEx, HousE).

Conclusiones:

El marco CoDiSS propuesto mejora los modelos de incrustación de KG al aprender de negativos informativos y mitigar los falsos negativos.
CoDiSS conduce a incrustaciones de KG más expresivas.
Este enfoque ofrece una dirección prometedora para avanzar en las técnicas de incrustación de KG.