Transforming arbitrary tables into logical form with TARTAR
Data & Knowledge Engineering
Web Semantics: Science, Services and Agents on the World Wide Web
Triplify: light-weight linked data publication from relational databases
Proceedings of the 18th international conference on World wide web
Converting governmental datasets into linked data
Proceedings of the 6th International Conference on Semantic Systems
Enabling interoperability of government data catalogues
EGOV'10 Proceedings of the 9th IFIP WG 8.5 international conference on Electronic government
TWC LOGD: A portal for linked open government data ecosystems
Web Semantics: Science, Services and Agents on the World Wide Web
A publishing pipeline for linked government data
ESWC'12 Proceedings of the 9th international conference on The Semantic Web: research and applications
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Governments and public administrations started recently to publish large amounts of structured data on the Web, mostly in the form of tabular data such as CSV files or Excel sheets. Various tools and projects have been launched aiming at facilitating the lifting of tabular data to reach semantically structured and linked data. However, none of these tools supported a truly incremental, pay-as-you-go data publication and mapping strategy, which enables effort sharing between data owners, community experts and consumers. In this article, we present an approach for enabling the user-driven semantic mapping of large amounts tabular data. We devise a simple mapping language for tabular data, which is easy to understand even for casual users, but expressive enough to cover the vast majority of potential tabular mappings use cases. We outline a formal approach for mapping tabular data to RDF. Default mappings are automatically created and can be revised by the community using a semantic wiki. The mappings are executed using a sophisticated streaming RDB2RDF conversion. We report about the deployment of our approach at the Pan-European data portal PublicData.eu, where we transformed and enriched almost 10,000 datasets accounting for 7.3 billion triples.