Tailoring a Knowledge Discovery Framework to Process Pharmacologic Documents

• Autores: Isabel Moreno, Alejandro Piad Morffis, Yoan Gutiérrez Vázquez , Paloma Moreda Pozo
• Localización: Procesamiento del lenguaje natural, ISSN 1135-5948, Nº. 74, 2025, págs. 43-54
• Idioma: inglés
• Títulos paralelos:
  • Adaptación de un Marco de Descubrimiento de Conocimiento para Procesar Documentos Farmacológicos
• Enlaces
  • Texto completo
• Resumen
  • español

    Este trabajo presenta un marco especializado de descubrimiento de conocimiento diseñado para procesar documentos técnicos de salud. Este mejora la tecnología existente, conocida como LETO, mediante la integración de CARMEN, un sistema de clasificación de entidades multilingüe capaz de incorporar semántica relacionada con la salud. Este enfoque colaborativo permite la generación de grafos de conocimiento específicos del dominio en dos idiomas, español e inglés. Además, ofrece un medio valioso para explorar relaciones dentro del ámbito de la salud que, de otro modo, podrían permanecer sin descubrir. La tecnología resultante se somete a un procedimiento de evaluación utilizando métricas estándar empleadas en tareas de descubrimiento de conocimiento, demostrando cómo CARMEN contribuye a aumentar el conocimiento descubierto en LETO. Así, el grafo de conocimiento generado puede aprovecharse para usos de representación explicativa, facilitando una articulación más completa del conocimiento humano y, entre otros fines, sirviendo potencialmente como un recurso educativo.

  • English

    This paper introduces a specialized knowledge discovery framework designed to process health technical documents and extract knowledge. The framework improves existing technology, known as LETO, through the integration of CARMEN, a multilingual entity classification system capable of infusing health-related semantics into the initial versatile approach. This collaborative approach enables the generation of domain-specific knowledge graphs for two languages, Spanish and English. Additionally, this provides a valuable means by which to explore relationships within the health domain that could otherwise remain undiscovered. The resulting technology is subjected to an evaluation procedure using standard metrics employed in knowledge discovery tasks, illustrating how CARMEN contributes to an augmentation in the knowledge discovered in LETO. Thus, the generated knowledge graph can be leveraged for the creation of explanatory representation techniques, facilitating a more comprehensive articulation of human knowledge and potentially serving, among other purposes, as an educational resource.

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