Developing the Quality Model for Collaborative Open Data

GE, Mouzhi and Wlodzimierz LEWONIEWSKI. Developing the Quality Model for Collaborative Open Data. Online. In Proceedings of the 24th International Conference on Knowledge-Based and Intelligent Information and Engineering Systems - KES 2020. 176th ed. Verona, Italy: Elsevier Procedia Computer Science, 2020, p. 1883-1892. ISSN 1877-0509. Available from: https://dx.doi.org/10.1016/j.procs.2020.09.228.

Basic information

• Original name: Developing the Quality Model for Collaborative Open Data
• Authors: GE, Mouzhi (156 China, guarantor, belonging to the institution) and Wlodzimierz LEWONIEWSKI (616 Poland).
• Edition: 176. vyd. Verona, Italy, Proceedings of the 24th International Conference on Knowledge-Based and Intelligent Information and Engineering Systems - KES 2020, p. 1883-1892, 10 pp. 2020.
• Publisher: Elsevier Procedia Computer Science

Other information

• Original language: English
• Type of outcome: Proceedings paper
• Field of Study: 10201 Computer sciences, information science, bioinformatics
• Confidentiality degree: is not subject to a state or trade secret
• Publication form: electronic version available online
• RIV identification code: RIV/00216224:14330/20:00115620
• Organization unit: Faculty of Informatics
• ISSN: 1877-0509
• Doi: http://dx.doi.org/10.1016/j.procs.2020.09.228
• Keywords in English: Data Quality Quality Assessment Collaborative Open Data Wikipedia Quality Model
• Tags: core_B, firank_B
• Tags: International impact, Reviewed

Abstract

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.

Links

• EF16_013/0001802, research and development project: Name: CERIT Scientific Cloud