Methodology for Converting and Publish Tabular Data into SKOS Resources via Python Notebooks

This paper presents a methodology for creating and converting tabular data into SKOS linguistic resources using Python notebooks. Designed to support users with limited technical skills, the approach offers a structured and reproducible process through interactive notebooks. The methodology covers metadata preparation, data scraping from repositories, information mapping, and data normalization for standardized vocabulary publication. Various specialized multilingual vocabularies, including those related to textile description and smart city terminology, were analyzed to evaluate the approach. Guidelines were also developed to optimize LOD environment deployment, including resource uploading and web application configuration. The methodology supports linguistic data management as Linked Open Data, offering a platform for hosting SKOS resources and training users to create structured data efficiently. The system was tested through external user engagement, demonstrating its scientific relevance and practical utility