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@inproceedings{1654177,
author = {Ge, Mouzhi and Lewoniewski, Wlodzimierz},
address = {Verona, Italy},
booktitle = {Proceedings of the 24th International Conference on Knowledge-Based and Intelligent Information and Engineering Systems - KES 2020},
doi = {http://dx.doi.org/10.1016/j.procs.2020.09.228},
edition = {176},
keywords = {Data Quality Quality Assessment Collaborative Open Data Wikipedia Quality Model},
howpublished = {elektronická verze "online"},
language = {eng},
location = {Verona, Italy},
pages = {1883-1892},
publisher = {Elsevier Procedia Computer Science},
title = {Developing the Quality Model for Collaborative Open Data},
year = {2020}
}
TY - JOUR
ID - 1654177
AU - Ge, Mouzhi - Lewoniewski, Wlodzimierz
PY - 2020
TI - Developing the Quality Model for Collaborative Open Data
PB - Elsevier Procedia Computer Science
CY - Verona, Italy
KW - Data Quality Quality Assessment Collaborative Open Data Wikipedia Quality Model
N2 - 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.
ER -
GE, Mouzhi a Wlodzimierz LEWONIEWSKI. Developing the Quality Model for Collaborative Open Data. Online. In \textit{Proceedings of the 24th International Conference on Knowledge-Based and Intelligent Information and Engineering Systems - KES 2020}. 176. vyd. Verona, Italy: Elsevier Procedia Computer Science, 2020, s.~1883-1892. ISSN~1877-0509. Dostupné z: https://dx.doi.org/10.1016/j.procs.2020.09.228.
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