Authors: Nagender Yamsani
Abstract: Organizations that rely on enterprise master data platforms often encounter persistent limitations in metadata quality, particularly in areas such as semantic clarity, contextual relevance, and cross domain interpretability. This study examines the use of natural language processing to enable context aware metadata enrichment within EBX repositories, addressing the challenge of transforming fragmented descriptive fields into structured, meaningful knowledge assets. The purpose of this research is to design and evaluate a systematic enrichment approach that can interpret textual attributes, infer relationships, and enhance metadata usability for governance, integration, and analytics. A mixed research method was applied, combining architectural modeling, controlled prototype implementation, and qualitative assessment of stewardship workflows in simulated enterprise scenarios. Observed outcomes demonstrate measurable improvements in classification consistency, metadata coverage, and retrieval efficiency, while also reducing dependence on manual interpretation. The proposed framework introduces a scalable enrichment pipeline that integrates linguistic analysis, semantic mapping, and governance driven validation within the operational lifecycle of EBX master data. This study argues that embedding language aware intelligence into metadata management practices can significantly strengthen data reliability and transparency. The findings provide a foundation for future research on semantic infrastructure in enterprise data ecosystems and offer practical guidance for organizations seeking to modernize metadata governance in complex master data environments.
DOI: https://doi.org/10.5281/zenodo.19849787
