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Erschienen in:
2012 | OriginalPaper | Buchkapitel
What Should I Link to? Identifying Relevant Sources and Classes for Data Linking
verfasst von : Andriy Nikolov, Mathieu d’Aquin, Enrico Motta
Erschienen in: The Semantic Web
Verlag: Springer Berlin Heidelberg
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With more data repositories constantly being published on the Web, choosing appropriate data sources to interlink with newly published datasets becomes a non-trivial problem. It is necessary to choose both the repositories to link to and the relevant subsets of these repositories, which contain potentially matching individuals. In order to do this, detailed information about the content and structure of semantic repositories is often required. However, retrieving and processing such information for a potentially large number of datasets is practically unfeasible. In this paper, we propose an approach which utilises an existing semantic web index in order to identify potentially relevant datasets for interlinking and rank them. Furthermore, we adapt instance-based ontology schema matching to extract relevant subsets of selected data source and, in this way, pre-configure data linking tools.