Personalised Access to Linked Data

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21240%2F14%3A00221725" target="_blank" >RIV/68407700:21240/14:00221725 - isvavai.cz</a>

Výsledek na webu

<a href="http://link.springer.com/chapter/10.1007/978-3-319-13704-9_10" target="_blank" >http://link.springer.com/chapter/10.1007/978-3-319-13704-9_10</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1007/978-3-319-13704-9_10" target="_blank" >10.1007/978-3-319-13704-9_10</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Personalised Access to Linked Data

Popis výsledku v původním jazyce

Recent efforts in the Semantic Web community have been primarily focused on developing technical infrastructure and technologies for efficient Linked Data acquisition, publishing and interlinking. Nevertheless, due to the huge and diverse amount of information, the actual access to a piece of information in the LOD cloud still demands significant amount of effort. In this paper, we present a novel configurable method for personalised access to Linked Data. The method recommends resources of interest from users with similar tastes. To measure the similarity between the users we introduce a novel resource semantic similarity metric, which takes into account the commonalities and informativeness of the resources. We validate and evaluate the method on a real-world dataset from the Web services domain. The results show that our method outperforms the other baseline methods in terms of accuracy, serendipity and diversity.

Název v anglickém jazyce

Personalised Access to Linked Data

Popis výsledku anglicky

Recent efforts in the Semantic Web community have been primarily focused on developing technical infrastructure and technologies for efficient Linked Data acquisition, publishing and interlinking. Nevertheless, due to the huge and diverse amount of information, the actual access to a piece of information in the LOD cloud still demands significant amount of effort. In this paper, we present a novel configurable method for personalised access to Linked Data. The method recommends resources of interest from users with similar tastes. To measure the similarity between the users we introduce a novel resource semantic similarity metric, which takes into account the commonalities and informativeness of the resources. We validate and evaluate the method on a real-world dataset from the Web services domain. The results show that our method outperforms the other baseline methods in terms of accuracy, serendipity and diversity.

Druh

D - Stať ve sborníku

CEP obor

—

OECD FORD obor

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Projekt

—

Návaznosti

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

Knowledge Engineering and Knowledge Management

ISBN

978-3-319-13703-2

ISSN

0302-9743

e-ISSN

—

Počet stran výsledku

16

Strana od-do

121-136

Název nakladatele

Springer International Publishing AG

Místo vydání

Cham

Místo konání akce

Linköping

Datum konání akce

24. 11. 2014

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

000354879800010