A Retrieval-Augmented Framework for Executable Context-Aware Data Quality Assessment

Context-aware data quality assessment enables the selection of quality checks that are aligned with data usage scenarios. While existing frameworks can model context knowledge and derive context-aware data quality assessment plans, their operationalization into executable validation logic remains largely manual. This paper presents a retrieval-augmented execution framework that bridges this gap by automatically translating validated context-aware assessment plans into executable code. The proposed approach embeds and retrieves dataset context representations, while preserving assessment plans as authoritative knowledge stored in a Neo4j knowledge graph. Retrieved assessment plans are combined with the input dataset context to form a structured augmented prompt that strictly constrains a large language model to act as a translator rather than a reasoning or decision-making component. The model is used solely to generate executable validation logic without introducing new quality dimensions or assumptions. The framework is evaluated using a real radiation wireless sensor dataset collected from a monitoring station. The evaluation demonstrates the feasibility of generating valid and executable Python data quality validation code.