Adaptación de un Marco de Descubrimiento de Conocimiento para Procesar Documentos Farmacológicos

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.