Developing the Quality Model for Collaborative Open Data

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216224%3A14330%2F20%3A00115620" target="_blank" >RIV/00216224:14330/20:00115620 - isvavai.cz</a>

Výsledek na webu

<a href="http://dx.doi.org/10.1016/j.procs.2020.09.228" target="_blank" >http://dx.doi.org/10.1016/j.procs.2020.09.228</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1016/j.procs.2020.09.228" target="_blank" >10.1016/j.procs.2020.09.228</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Developing the Quality Model for Collaborative Open Data

Popis výsledku v původním jazyce

Nowadays, the development of data sharing technologies allows to involve more people to collaboratively contribute knowledge on the Web. The shared knowledge is usually represented as Collaborative Open Data (COD), for example, Wikipedia is one of the well-known sources for COD. The Wikipedia articles can be written in different languages, updated in real time, and originated from a vast variety of editors. However, COD also bring different data quality problems such as data inconsistency and low data objectiveness due to the crowd-based and dynamic nature. These data quality problems such as biased information may lead to sentimental changes or social impacts. This paper therefore proposes a new measurement model to assess the quality of COD. In order to evaluate the proposed model, A preliminary experiment is conducted with a large scale of Wikipedia articles to validate the applicability and efficiency of this proposed quality model in the real-world scenario.

Název v anglickém jazyce

Developing the Quality Model for Collaborative Open Data

Popis výsledku anglicky

Nowadays, the development of data sharing technologies allows to involve more people to collaboratively contribute knowledge on the Web. The shared knowledge is usually represented as Collaborative Open Data (COD), for example, Wikipedia is one of the well-known sources for COD. The Wikipedia articles can be written in different languages, updated in real time, and originated from a vast variety of editors. However, COD also bring different data quality problems such as data inconsistency and low data objectiveness due to the crowd-based and dynamic nature. These data quality problems such as biased information may lead to sentimental changes or social impacts. This paper therefore proposes a new measurement model to assess the quality of COD. In order to evaluate the proposed model, A preliminary experiment is conducted with a large scale of Wikipedia articles to validate the applicability and efficiency of this proposed quality model in the real-world scenario.

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

<a href="/cs/project/EF16_013%2F0001802" target="_blank" >EF16_013/0001802: CERIT Scientific Cloud</a><br>

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Rok uplatnění

2020

Kód důvěrnosti údajů

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

Název statě ve sborníku

Proceedings of the 24th International Conference on Knowledge-Based and Intelligent Information and Engineering Systems - KES 2020

ISBN

—

ISSN

1877-0509

e-ISSN

—

Počet stran výsledku

10

Strana od-do

1883-1892

Název nakladatele

Elsevier Procedia Computer Science

Místo vydání

Verona, Italy

Místo konání akce

Verona, Italy

Datum konání akce

1. 1. 2020

Typ akce podle státní příslušnosti

CST - Celostátní akce

Kód UT WoS článku

—