BLANCO SÁNCHEZ, José Miguel, Mouzhi GE and Tomáš PITNER. Human-Generated Web Data Disentanglement for Complex Event Processing. In 26th International Conference on Knowledge-Based and Intelligent Information and Engineering Systems, KES 2022. Verona, Italy: Elsevier, 2022, p. 1341-1349. ISSN 1877-0509. Available from: https://dx.doi.org/10.1016/j.procs.2022.09.190.
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Basic information
Original name Human-Generated Web Data Disentanglement for Complex Event Processing
Authors BLANCO SÁNCHEZ, José Miguel (724 Spain, guarantor, belonging to the institution), Mouzhi GE and Tomáš PITNER (203 Czech Republic, belonging to the institution).
Edition Verona, Italy, 26th International Conference on Knowledge-Based and Intelligent Information and Engineering Systems, KES 2022, p. 1341-1349, 9 pp. 2022.
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 printed version "print"
RIV identification code RIV/00216224:14330/22:00126674
Organization unit Faculty of Informatics
ISSN 1877-0509
Doi http://dx.doi.org/10.1016/j.procs.2022.09.190
Keywords in English Complex Event Processing; Semantic Web; Data Disentanglement; Web Data Preprocessing;
Tags core_B, firank_B
Changed by Changed by: RNDr. Pavel Šmerk, Ph.D., učo 3880. Changed: 6/4/2023 07:36.
Abstract
In social media, human-generated web data from real-world events have become exponentially complex due to the chaotic and spontaneous features of natural language. This may create an information overload for the information consumers, and in turn not easily digest a large amount of information in a limited time. To tackle this issue, we propose to use Complex Event Processing (CEP) and semantic web reasoners to disentangle the human-generated data and present users with only relevant and important data. However, one of the key obstacles is that the human-generated data can have no structured meaning sometimes even for the speaker, hindering the output of the CEP. Therefore, in order to adapt to the CEP inputs, we present two different techniques that allow for the discrimination and digestion of value of human-generated data. The first one relies on the Variable Sharing Property that was developed for relevance logics, while the second one is based on semantic equivalence and natural language processing. The results can be given to CEP for further semantic reasoning and generate digested information for users.
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