Human-Generated Web Data Disentanglement for Complex Event
Processing

D 2022

Human-Generated Web Data Disentanglement for Complex Event Processing

BLANCO SÁNCHEZ, José Miguel, Mouzhi GE and Tomáš PITNER

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

Language

English

Type of outcome

Stať ve sborníku

Field of Study

10201 Computer sciences, information science, bioinformatics

Country of publisher

Netherlands

Confidentiality degree

není předmětem státního či obchodního tajemství

Publication form

printed version "print"

RIV identification code

RIV/00216224:14330/22:00126674

Organization unit

Faculty of Informatics

ISSN

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;

Abstract

V originále

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.

Citovat

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.

@inproceedings{2217810,
   author = {Blanco Sánchez, José Miguel and Ge, Mouzhi and Pitner, Tomáš},
   address = {Verona, Italy},
   booktitle = {26th International Conference on Knowledge-Based and Intelligent Information and Engineering Systems, KES 2022},
   doi = {http://dx.doi.org/10.1016/j.procs.2022.09.190},
   keywords = {Complex Event Processing; Semantic Web; Data Disentanglement; Web Data Preprocessing;},
   howpublished = {tištěná verze "print"},
   language = {eng},
   location = {Verona, Italy},
   pages = {1341-1349},
   publisher = {Elsevier},
   title = {Human-Generated Web Data Disentanglement for Complex Event Processing},
   year = {2022}
}

TY  - JOUR
ID  - 2217810
AU  - Blanco Sánchez, José Miguel - Ge, Mouzhi - Pitner, Tomáš
PY  - 2022
TI  - Human-Generated Web Data Disentanglement for Complex Event Processing
PB  - Elsevier
CY  - Verona, Italy
KW  - Complex Event Processing
KW  - Semantic Web
KW  - Data Disentanglement
KW  - Web Data Preprocessing;
N2  - 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.
ER  -

BLANCO SÁNCHEZ, José Miguel, Mouzhi GE and Tomáš PITNER. Human-Generated Web Data Disentanglement for Complex Event Processing. In \textit{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.

Detailed Information on Publication Record