What's all the Data about? - Creating Structured Profiles of Linked Data on the Web (Abstract/Poster in atti di convegno)

Type
Label
  • What's all the Data about? - Creating Structured Profiles of Linked Data on the Web (Abstract/Poster in atti di convegno) (literal)
Anno
  • 2014-01-01T00:00:00+01:00 (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#doi
  • 10.1145/2567948.2577334 (literal)
Alternative label
  • Besnik Fetahu, Stefan Dietze, Bernardo Pereira Nunes, Marco Antonio Casanova, Davide Taibi, Wolfgang Nejdl (2014)
    What's all the Data about? - Creating Structured Profiles of Linked Data on the Web
    in 23rd international conference on World Wide Web - WWW2014, Seoul (Korea), April 7 - 11, 2014
    (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#autori
  • Besnik Fetahu, Stefan Dietze, Bernardo Pereira Nunes, Marco Antonio Casanova, Davide Taibi, Wolfgang Nejdl (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#url
  • http://wwwconference.org/proceedings/www2014/companion/p261.pdf (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#titoloVolume
  • WWW Companion '14: Proceedings of the companion publication of the 23rd international conference on World wide web companion (literal)
Note
  • Poster (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#affiliazioni
  • L3S Research Center, Leibniz Universität Hannover, Hannover, Germany Pontifical Catholic University of Rio de Janeiro, Rio de Janeiro, Brazil Institute for Educational Technologies, CNR, Palermo Italy (literal)
Titolo
  • What's all the Data about? - Creating Structured Profiles of Linked Data on the Web (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#isbn
  • 978-1-4503-2745-9 (literal)
Abstract
  • The emergence of the Web of Data, in particular Linked Open Data (LOD) [1], has led to an abundance of data available on the Web. Data is shared as part of datasets, often containing inter-dataset links [6], mostly concentrated on established datasets, such as DBpedia. Datasets vary significantly with respect to represented resource types, currentness, coverage of topics and domains, size, used languages, coherence, accessibility [3] or general quality aspects. The challenges from such diversity are underlined by the limited reuse of datasets from the LOD Cloud, where reuse and linking often focus on well-known datasets like DBpedia. Therefore, descriptive and reliable metadata are paramount to enable targeted search, assessment and reuse of datasets. To address these issues and building up on earlier work [4], we propose an automated approach for creating structured profiles describing the topic coverage of individual datasets. The proposed approach considers a combination of sampling, topic extraction and topic ranking techniques. The sampling process is used to determine the best trade-off between scalability and profiling accuracy. Topic ranking is based on an adoption of graphical models PageRank, K-Step Markov, and HITS, which introduces prior knowledge into the computation of vertex importance [7]. Finally, the generated profiles are exposed as part of a public dataset based on the Vocabulary of Interlinked Datasets (VoID)and the newly introduced vocabulary of links (VoL) which describes the degree of relatedness between datasets and topics. (literal)
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