An automatic key discovery approach for data linking |
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Affiliation: | 1. Department of Computer Science, University of Houston, Houston, TX, United States;2. MINES ParisTech, PSL Research University, France;1. School of Mechanical Engineering, Pusan National University, Busan 609-735, South Korea;2. McCormick School of Engineering and Applied Science, Northwestern University, Evanston, IL 60208, USA;1. School of Engineering & Advanced Technology, Massey University, New Zealand;2. Department of Mathematics, The University of Flensburg, Germany;3. Department of Computer Science, The University of Auckland, New Zealand;4. IRIT, CNRS and Université de Toulouse III, France |
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Abstract: | In the context of Linked Data, different kinds of semantic links can be established between data. However when data sources are huge, detecting such links manually is not feasible. One of the most important types of links, the identity link, expresses that different identifiers refer to the same real world entity. Some automatic data linking approaches use keys to infer identity links, nevertheless this kind of knowledge is rarely available. In this work we propose KD2R, an approach which allows the automatic discovery of composite keys in RDF data sources that may conform to different schemas. We only consider data sources for which the Unique Name Assumption is fulfilled. The obtained keys are correct with respect to the RDF data sources in which they are discovered. The proposed algorithm is scalable since it allows the key discovery without having to scan all the data. KD2R has been tested on real datasets of the international contest OAEI 2010 and on datasets available on the web of data, and has obtained promising results. |
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Keywords: | Data linking Identity links Keys Ontology |
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