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DING! Dataset Ranking using Formal Descriptions

ARAN - Access to Research at NUI Galway

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dc.contributor.author Toupikov, Nickolai en
dc.contributor.author Umbrich, Jürgen en
dc.contributor.author Delbru, Renaud en
dc.contributor.author Hausenblas, Michael en
dc.contributor.author Tummarello, Giovanni en
dc.date.accessioned 2009-12-15T16:24:41Z en
dc.date.available 2009-12-15T16:24:41Z en
dc.date.issued 2009 en
dc.identifier.citation Nickolai Toupikov, Jürgen Umbrich , Renaud Delbru, Michael Hausenblas, Giovanni Tummarello "DING! Dataset Ranking using Formal Descriptions", Linked Data on the Web Workshop (LDOW 09), in conjunction with 18th International World Wide Web Conference (WWW 09), 2009. en
dc.identifier.uri http://hdl.handle.net/10379/541 en
dc.description.abstract Considering that thousands if not millions of linked datasets will be published soon, we motivate in this paper the need for an efficient and effective way to rank interlinked datasets based on formal descriptions of their characteristics. We propose DING (from Dataset RankING) as a new approach to rank linked datasets using information provided by the voiD vocabulary. DING is a domain-independent link anal- ysis that measures the popularity of datasets by considering the cardinality and types of the relationships. We propose also a methodology to automatically assign weights to link types. We evaluate the proposed ranking algorithm against other well known ones, such as PageRank or HITS, using synthetic voiD descriptions. Early results show that DING performs better than the standardWeb ranking algorithms. en
dc.format application/pdf en
dc.language.iso en en
dc.subject.lcsh World Wide Web en
dc.subject.lcsh Algorithms en
dc.title DING! Dataset Ranking using Formal Descriptions en
dc.type Workshop paper en
dc.description.peer-reviewed peer-reviewed en
dc.contributor.funder Romulus en
dc.contributor.funder OKKAM en
dc.contributor.funder Science Foundation Ireland en

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