BLANCO SÁNCHEZ, José Miguel, Mouzhi GE and Tomáš PITNER. Modeling Inconsistent Data for Reasoners in Web of Things. Online. In Procedia Computer Science, Volume 192, 25th KES International Conference on Knowledge-Based and Intelligent Information and Engineering Systems, KES 2021. Szczecin: Elsevier, 2021, p. 1265-1273. ISSN 1877-0509. Available from: https://dx.doi.org/10.1016/j.procs.2021.08.130.
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Basic information
Original name Modeling Inconsistent Data for Reasoners in Web of Things
Authors BLANCO SÁNCHEZ, José Miguel (724 Spain, guarantor, belonging to the institution), Mouzhi GE (156 China) and Tomáš PITNER (203 Czech Republic, belonging to the institution).
Edition Szczecin, Procedia Computer Science, Volume 192, 25th KES International Conference on Knowledge-Based and Intelligent Information and Engineering Systems, KES 2021, p. 1265-1273, 9 pp. 2021.
Publisher Elsevier
Other information
Original language English
Type of outcome Proceedings paper
Field of Study 10201 Computer sciences, information science, bioinformatics
Country of publisher Netherlands
Confidentiality degree is not subject to a state or trade secret
Publication form electronic version available online
RIV identification code RIV/00216224:14330/21:00122772
Organization unit Faculty of Informatics
ISSN 1877-0509
Doi http://dx.doi.org/10.1016/j.procs.2021.08.130
UT WoS 000720289001032
Keywords in English Web of Things Internet of Things Semantic Web Reasoners
Tags firank_B
Changed by Changed by: RNDr. Pavel Šmerk, Ph.D., učo 3880. Changed: 2/5/2022 15:41.
Abstract
With the recent developments of the Internet of Things and its integration in the web environment, the Web of Things and the real-time data submissions to Reasoners are enabled. However, the data that are fed to the Reasoners are often inconsistent. This can be possibly caused by the malfunction of certain Internet of Things device or by human errors. The data consistency issue is becoming more complex in the Web of Things network. This paper, therefore, proposes a new data processing model to tackle the inconsistent data, so that the processed data can be further used in Reasoners. The data processing model introduces an oversimplification of the Shramko-Wansing sixteen-valued trilattice, which is an extension of Belnap’s four-valued bilattice to assign the data classical truth-values. A preliminary implementation is demonstrated to validate the proposed model. The result shows that our model can avoid system collapse when contradictory outputs exist.
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