Exploration of Document Classification with Linked Data and pageRank

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F49777513%3A23520%2F14%3A43919202" target="_blank" >RIV/49777513:23520/14:43919202 - isvavai.cz</a>

Výsledek na webu

<a href="http://dx.doi.org/10.1007/978-3-319-01571-2_6" target="_blank" >http://dx.doi.org/10.1007/978-3-319-01571-2_6</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1007/978-3-319-01571-2_6" target="_blank" >10.1007/978-3-319-01571-2_6</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Exploration of Document Classification with Linked Data and pageRank

Popis výsledku v původním jazyce

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 ontologyor 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 isused 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.

Název v anglickém jazyce

Exploration of Document Classification with Linked Data and pageRank

Popis výsledku anglicky

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 ontologyor 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 isused 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.

Druh

D - Stať ve sborníku

CEP obor

IN - Informatika

OECD FORD obor

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Projekt

Výsledek vznikl pri realizaci vícero projektů. Více informací v záložce Projekty.

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)<br>S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2014

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název statě ve sborníku

Intelligent Distributed Computing VII

ISBN

978-3-319-01570-5

ISSN

1860-949X

e-ISSN

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Počet stran výsledku

7

Strana od-do

37-43

Název nakladatele

Springer

Místo vydání

Cham

Místo konání akce

Praha

Datum konání akce

4. 9. 2013

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

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