Exploration of Document Classification with Linked Data and PageRank
Abstract: 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.
Keywords: classification, Linked Data, PageRank, feature selection
Year: 2014
Authors of this publication:
Martin Dostal
E-mail: madostal@kiv.zcu.cz
Michal Nykl
E-mail: nyklm@kiv.zcu.cz
WWW: http://home.zcu.cz/~nyklm/
Karel Ježek
Phone: +420 377632475
E-mail: jezek_ka@kiv.zcu.cz
WWW: https://cs.wikipedia.org/wiki/Karel_Je%C5%BEek_(informatik)
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Document Clustering and Linked Data | |
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Desc.: | Unsupervised methods for automatic tagging and clustering based on information extraction from Linked data. |
Social Networks Analysis | |
Authors: | Karel Ježek, Dalibor Fiala, Michal Nykl |
Desc.: | Application of the PageRank algorithm and its modifications to the exploration of network structures, particularly citation and co-autorship networks. |