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 a Tomáš PITNER

Základní údaje

Originální název

Human-Generated Web Data Disentanglement for Complex Event Processing

Autoři

BLANCO SÁNCHEZ, José Miguel (724 Španělsko, garant, domácí), Mouzhi GE a Tomáš PITNER (203 Česká republika, domácí)

Vydání

Verona, Italy, 26th International Conference on Knowledge-Based and Intelligent Information and Engineering Systems, KES 2022, od s. 1341-1349, 9 s. 2022

Nakladatel

Elsevier

Další údaje

Jazyk

angličtina

Typ výsledku

Stať ve sborníku

Obor

10201 Computer sciences, information science, bioinformatics

Stát vydavatele

Nizozemské království

Utajení

není předmětem státního či obchodního tajemství

Forma vydání

tištěná verze "print"

Kód RIV

RIV/00216224:14330/22:00126674

Organizační jednotka

Fakulta informatiky

ISSN

DOI

http://dx.doi.org/10.1016/j.procs.2022.09.190

Klíčová slova anglicky

Complex Event Processing; Semantic Web; Data Disentanglement; Web Data Preprocessing;

Štítky

core_B, firank_B

Změněno: 6. 4. 2023 07:36, RNDr. Pavel Šmerk, Ph.D.

Anotace

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 a 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, s. 1341-1349. ISSN 1877-0509. Dostupné z: 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 a 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, s.~1341-1349. ISSN~1877-0509. Dostupné z: https://dx.doi.org/10.1016/j.procs.2022.09.190.

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