About: Exploration of Document Classification with Linked Data and pageRank     Goto   Sponge   NotDistinct   Permalink

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  • In this article, we would like to present a new approach to classification using Linked Data and PageRank. Our research is focused on classification methods that are enhanced by semantic information. The semantic information can be obtained from ontology or from Linked Data. DBpedia was used as a source of Linked Data in our case. The feature selection method is semantically based so features can be recognized by non-professional users as they are in a human readable and understandable form. PageRank is used during the feature selection and generation phase for the expansion of basic features into more general representatives. This means that feature selection and PageRank processing is based on network relations obtained from Linked Data. The discovered features can be used by standard classification algorithms. We will present promising results that show the simple applicability of this approach to two different datasets.
  • In this article, we would like to present a new approach to classification using Linked Data and PageRank. Our research is focused on classification methods that are enhanced by semantic information. The semantic information can be obtained from ontology or from Linked Data. DBpedia was used as a source of Linked Data in our case. The feature selection method is semantically based so features can be recognized by non-professional users as they are in a human readable and understandable form. PageRank is used during the feature selection and generation phase for the expansion of basic features into more general representatives. This means that feature selection and PageRank processing is based on network relations obtained from Linked Data. The discovered features can be used by standard classification algorithms. We will present promising results that show the simple applicability of this approach to two different datasets. (en)
Title
  • Exploration of Document Classification with Linked Data and pageRank
  • Exploration of Document Classification with Linked Data and pageRank (en)
skos:prefLabel
  • Exploration of Document Classification with Linked Data and pageRank
  • Exploration of Document Classification with Linked Data and pageRank (en)
skos:notation
  • RIV/49777513:23520/14:43919202!RIV15-GA0-23520___
http://linked.open...avai/riv/aktivita
http://linked.open...avai/riv/aktivity
  • P(ED1.1.00/02.0090), P(GAP103/11/1489), S
http://linked.open...vai/riv/dodaniDat
http://linked.open...aciTvurceVysledku
http://linked.open.../riv/druhVysledku
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  • 16088
http://linked.open...ai/riv/idVysledku
  • RIV/49777513:23520/14:43919202
http://linked.open...riv/jazykVysledku
http://linked.open.../riv/klicovaSlova
  • classification, Linked Data, PageRank, feature selection (en)
http://linked.open.../riv/klicoveSlovo
http://linked.open...ontrolniKodProRIV
  • [86C33C73CBE7]
http://linked.open...v/mistoKonaniAkce
  • Praha
http://linked.open...i/riv/mistoVydani
  • Cham
http://linked.open...i/riv/nazevZdroje
  • Intelligent Distributed Computing VII
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http://linked.open...vavai/riv/projekt
http://linked.open...UplatneniVysledku
http://linked.open...iv/tvurceVysledku
  • Dostal, Martin
  • Ježek, Karel
  • Nykl, Michal
http://linked.open...vavai/riv/typAkce
http://linked.open.../riv/zahajeniAkce
issn
  • 1860-949X
number of pages
http://bibframe.org/vocab/doi
  • 10.1007/978-3-319-01571-2_6
http://purl.org/ne...btex#hasPublisher
  • Springer-Verlag
https://schema.org/isbn
  • 978-3-319-01570-5
http://localhost/t...ganizacniJednotka
  • 23520
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