Time-aware Link Prediction in RDF Graphs

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21240%2F15%3A00231357" target="_blank" >RIV/68407700:21240/15:00231357 - isvavai.cz</a>

Výsledek na webu

<a href="http://www.scitepress.org/Portal/PublicationsDetail.aspx?ID=0rKeL7JmT3g=&t=1" target="_blank" >http://www.scitepress.org/Portal/PublicationsDetail.aspx?ID=0rKeL7JmT3g=&t=1</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.5220/0005428403900401" target="_blank" >10.5220/0005428403900401</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Time-aware Link Prediction in RDF Graphs

Popis výsledku v původním jazyce

When a link is not explicitly present in an RDF dataset, it does not mean that the link could not exist in reality. Link prediction methods try to overcome this problem by finding new links in the dataset with support of a background knowledge about thealready existing links in the dataset. In dynamic environments that change often and evolve over time, link prediction methods should also take into account the temporal aspects of data. In this paper, we present a novel time-aware link prediction method. We model RDF data as a tensor and take into account the time when RDF data was created. We use an ageing function to model a retention of the information over the time; lower the significance of the older information and promote more recent. Our evaluation shows that the proposed method improves quality of predictions when compared with methods that do not consider the time information.

Název v anglickém jazyce

Time-aware Link Prediction in RDF Graphs

Popis výsledku anglicky

When a link is not explicitly present in an RDF dataset, it does not mean that the link could not exist in reality. Link prediction methods try to overcome this problem by finding new links in the dataset with support of a background knowledge about thealready existing links in the dataset. In dynamic environments that change often and evolve over time, link prediction methods should also take into account the temporal aspects of data. In this paper, we present a novel time-aware link prediction method. We model RDF data as a tensor and take into account the time when RDF data was created. We use an ageing function to model a retention of the information over the time; lower the significance of the older information and promote more recent. Our evaluation shows that the proposed method improves quality of predictions when compared with methods that do not consider the time information.

Druh

D - Stať ve sborníku

CEP obor

IN - Informatika

OECD FORD obor

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Projekt

—

Návaznosti

S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2015

Kód důvěrnosti údajů

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

Název statě ve sborníku

WEBIST 2015 - Proceedings of the 11th International Conference on Web Information Systems and Technologies

ISBN

978-989-758-106-9

ISSN

—

e-ISSN

—

Počet stran výsledku

12

Strana od-do

—

Název nakladatele

SciTePress

Místo vydání

Madeira

Místo konání akce

Lisbon

Datum konání akce

20. 5. 2015

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

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