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Lightweight Transformation of Tabular Open Data to RDF

Currently, most Open Government Data portals mainly offer data in tabular formats. These lack the benefits of Linked Data, expressed in RDF graphs. In this paper, we propose a fast and simple semi-automatic tabular-to-RDF mapping approach. We introduce an efficient transformation algorithm for finding optimal relations between columns based on ontology information. We deal with multilingual diversity between data sets by combining translators and thesauri to create context. Finally, we use efficient string and lexical matching approaches in a learning loop to ensure high performance with good precision.