Ademar Crotti Junior
ABSTRACT
Typically tools that map non-RDF data into RDF format rely on the technology native to the source of the data when manipulation of data during the mapping is required. Depending on the data format, data manipulation can be performed using underlying technology, such as RDBMS for relational databases or XPath for XML. For CSV/Tabular data there is no such underlying technology, and instead transforming the source data into another format or pre/post-processing techniques are used. As part of this paper, we present a comparison framework for the state-of-the-art in converting CSV/Tabular data into RDF, where a key feature evaluated is transformation functions. We argue that existing approaches for transformation functions in such tools are complex - in number of steps and tools involved - and therefore not as traceable and transparent as one would like. We tackle these problems by defining a more generic, usable and amenable method to incorporate functions into uplift mapping languages, called FunUL. As proof of concept, we show an implementation of our method. Moreover, by using a real world Digital Humanities case study, we compare our approach with other approaches that we have identified to include transformation functions as part of the mapping for CSV/Tabular data.
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Index Terms
- FunUL: a method to incorporate functions into uplift mapping languages
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