Artículos
Preprocesamiento automatizado de datos mediante reglas SWRL parametrizadas en RAG local
Resumen
Este trabajo formaliza el conocimiento experto en preprocesamiento de datos mediante OWL y reglas SWRL parametrizadas, integradas con una arquitectura RAG para consultas en lenguaje natural. Se diseñó una ontología OWL con 47 clases, 42 individuos y 19 reglas SWRL en Protégé, y se validó sobre el dataset Netflix Movies and TV Shows (8.807 registros, 12 columnas). Un motor de inferencia implementado en Python, que analiza sintácticamente la estructura de las reglas SWRL a partir de la serialización en RDF/XML y evalúa los antecedentes mediante condicionales deterministas, sin invocar un razonador OWL DL nativo, generó 15 recomendaciones distribuidas en nueve columnas analizables; las tres columnas restantes (show_id, title, description) fueron excluidas por corresponder, respectivamente, a un identificador y a campos de texto libre. El sistema orienta a científicos de datos en la selección de técnicas según el perfil estadístico de cada columna, lo que reduce la dependencia de la configuración manual. La novedad radica en la parametrización de reglas SWRL con métricas estadísticas dinámicas, combinada con la recuperación semántica multilingüe en un pipeline RAG local que favorece la trazabilidad y la privacidad.
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Referencias
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