Semantics and complexity of SPARQL
ACM Transactions on Database Systems (TODS)
Feedback-based annotation, selection and refinement of schema mappings for dataspaces
Proceedings of the 13th International Conference on Extending Database Technology
Journal on data semantics VIII
Automatically incorporating new sources in keyword search-based data integration
Proceedings of the 2010 ACM SIGMOD International Conference on Management of data
Data integration with dependent sources
Proceedings of the 14th International Conference on Extending Database Technology
Using information quality for the identification of relevant web data sources: a proposal
Proceedings of the 14th International Conference on Information Integration and Web-based Applications & Services
Identifying candidate datasets for data interlinking
ICWE'13 Proceedings of the 13th international conference on Web Engineering
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The huge and growing volume of linked data is increasing the interest in developing applications on top of such data. One of the distinguishing features of linked data applications is that the data could come from any RDF data set available on the Web. Different from conventional applications, where the data sources are under control of the application's owner or developer, linked data applications follow the Semantic Web vision of a world full of reusable data. Considering a potentially large number of data sets, one of the primary challenges facing the development of such solutions is the identification of suitable data sources, i.e., data sets that could give a good contribution to the answer of user queries. In this paper, we discuss this problem and we present a feedback-based approach to incrementally identify new data sets for domain-specific linked data application.