Extracción de relaciones semánticas agrupadas de los textos para representar el conocimiento de las propiedades de las plantas medicinales en las redes sociales

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Chaveevan Pechsiri¹, Intaka Piriyakul², Joseph Santhi Pechsiri³

¹College of Innovative Technology and Engineering, Dhurakij Pundit University, Bangkok, Thailand.

Frontiers in artificial intelligence

|August 27, 2025

Resumen

Este estudio extrae las relaciones PlantPart-MedicinalPropertyGroup de los textos medicinales tailandeses, creando un gráfico de conocimiento para terapias alternativas. Este método ayuda a los usuarios a comprender las propiedades de las hierbas, especialmente durante las crisis de salud como el COVID-19.

Área de la Ciencia:

Procesamiento del lenguaje natural
La bioinformática
La etnobotánica

Sus antecedentes:

El conocimiento de las plantas medicinales es crucial para las terapias alternativas, especialmente durante las pandemias.
Los textos medicinales tailandeses a menudo expresan propiedades a través de frases verbales en Unidades de Discurso Elemental (EDU).
La extracción de las relaciones PlantPart-MedicinalPropertyGroup de estos textos presenta desafíos debido al lenguaje complejo y a la falta de anotaciones.

Objetivo del estudio:

Desarrollar un método para extraer las relaciones PlantPart-MedicinalPropertyGroup de los sitios web académicos de las farmacias.
Crear una representación gráfica del conocimiento de las propiedades de las plantas medicinales.
Ayudar a los usuarios y pacientes nativos a acceder a información médica alternativa.

Principales métodos:

Utilizó un Concepto de Verbo de Solución de HerbMed para identificar el concepto de EDU de propiedades medicinales.
Aplicación de patrones de co-ocurrencia de palabras (word-co) para crear una tabla de concepto de propiedades medicinales (MPC).
Utilizó el emparejamiento de cadenas y el modelado de ecuaciones estructurales para la extracción de relaciones.

Principales resultados:

Con éxito extraído PlantPart-MedicinalPropertyGroup relaciones con alta calidad.
Desarrolló un método para representar gráficamente el conocimiento de las propiedades de las plantas medicinales.
Demostró la viabilidad de la extracción automatizada de relaciones sin etiquetado explícito de clase de relación.

Conclusiones:

El enfoque propuesto extrae efectivamente conocimientos valiosos sobre plantas medicinales.
Este método puede mejorar la accesibilidad a la información sobre terapias alternativas.
La representación gráfica ayuda a comprender las complejas propiedades medicinales de las plantas.

Palabras clave:

Inteligencia artificial relación semántica-característica agrupada Procesamiento del lenguaje natural (informática)Modelado de ecuaciones estructurales Co-ocurrencia de palabras

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