Exploiting Temporal Dimension in Tensor-Based Link Prediction

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21240%2F16%3A00242596" target="_blank" >RIV/68407700:21240/16:00242596 - isvavai.cz</a>

Výsledek na webu

<a href="http://link.springer.com/chapter/10.1007/978-3-319-30996-5_11" target="_blank" >http://link.springer.com/chapter/10.1007/978-3-319-30996-5_11</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1007/978-3-319-30996-5_11" target="_blank" >10.1007/978-3-319-30996-5_11</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Exploiting Temporal Dimension in Tensor-Based Link Prediction

Popis výsledku v původním jazyce

In the recent years, there is a significant interest in a link prediction - an important task for graph-based data structures. Although there exist many approaches based on the graph theory and factorizations, there is still lack of methods that can work with multiple types of links and temporal information. The creation time of a link is an important aspect: it reflects age and credibility of the information. In this paper, we introduce a method that predicts missing links in RDF datasets. We model multiple relations of RDF as a tensor that incorporates the creation time of links as a key component too. We evaluate the proposed approach on real world datasets: an RDF representation of the ProgrammableWeb directory and a subset of the DBpedia focused on movies. The results show that the proposed method outperforms other link prediction approaches.

Název v anglickém jazyce

Exploiting Temporal Dimension in Tensor-Based Link Prediction

Popis výsledku anglicky

In the recent years, there is a significant interest in a link prediction - an important task for graph-based data structures. Although there exist many approaches based on the graph theory and factorizations, there is still lack of methods that can work with multiple types of links and temporal information. The creation time of a link is an important aspect: it reflects age and credibility of the information. In this paper, we introduce a method that predicts missing links in RDF datasets. We model multiple relations of RDF as a tensor that incorporates the creation time of links as a key component too. We evaluate the proposed approach on real world datasets: an RDF representation of the ProgrammableWeb directory and a subset of the DBpedia focused on movies. The results show that the proposed method outperforms other link prediction approaches.

Druh

D - Stať ve sborníku

CEP obor

IN - Informatika

OECD FORD obor

—

Projekt

—

Návaznosti

V - Vyzkumna aktivita podporovana z jinych verejnych zdroju

Rok uplatnění

2016

Kód důvěrnosti údajů

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

Název statě ve sborníku

Web Information Systems and Technologies

ISBN

978-3-319-30995-8

ISSN

1865-1348

e-ISSN

—

Počet stran výsledku

21

Strana od-do

211-231

Název nakladatele

Springer International Publishing

Místo vydání

Cham

Místo konání akce

Lisbon

Datum konání akce

20. 5. 2015

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

—