Semantic Lifting of CSV to RDF
Lifting the Tabular Data
Integrating and analysing large amounts of data plays an increasingly important role in today's society. Often, however, new discoveries and insights can only be attained by integrating information from dispersed sources, which requires considerable amounts of time and can be error prone if information is stored in heterogeneous representations.
The Semantic Web and Linked Data communities are advocating the use of RDF and Linked Data as a standardized data publication format facilitating data integration and visualization. Despite its unquestionable advantages, only a tiny fraction of open data is currently available as RDF. At the Pan-European data portal PublicData.eu, which aggregates dataset descriptions from numerous other European data portals, only 1,790 out of more than 49,000 datasets (i.e. just 4 %) were available as RDF. This can be mostly attributed to the fact, that publishing data as RDF requires additional eﬀort in particular with regard to identiﬁer creation, vocabulary design, reuse and mapping.
Various tools and projects (e.g. Any23, Triplify, Tabels, Open Reﬁne) have been launched aiming at facilitating the lifting of tabular data to reach semantically structured and interlinked data. However, none of these tools supported a truly incremental, pay-as-you-go data publication and mapping strategy, which enabled eﬀort sharing between data owners and consumers. The lack of such an architecture of participation with regard to the mapping and transformation of tabular data to semantically richer representations hampers the creation of an ecosystem for open data publishing and reuse. In order to realize such an ecosystem, we have to enable a large number of potential stakeholders to eﬀectively and eﬃciently collaborate in the data lifting process. Small contributions (such as ﬁne-tuning of a mapping conﬁguration or the mapping of an individual column) should be possible and render an instant beneﬁt for the respective stakeholder. The sum of many such small contributions should result in a comprehensive Open Knowledge space, where datasets are increasingly semantically structured and interlinked.
The approach presented in this section supports a truly incremental, payas-you-go data publication, mapping and visualization strategy, which enables eﬀort sharing between data owners, community experts and consumers. The transformation mappings are crowd-sourced using a Semantic MediaWiki and thus allow incremental quality improvement. The transformation process links related tabular data together and thus enables the navigation between heterogeneous sources. For visualization, we integrate CubeViz for statistical data and Facete for spatial data, which provide the users with the ability to perform simple data exploration tasks on the transformed tabular data. The application of our approach to the PublicData.eu portal results in 15,000 transformed datasets amounting to 7.3 Billion triples, thus adding a sizeable part to the Web of Data.