Human-Generated Web Data Disentanglement for Complex Event Processing

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216224%3A14330%2F22%3A00126674" target="_blank" >RIV/00216224:14330/22:00126674 - isvavai.cz</a>

Výsledek na webu

<a href="http://dx.doi.org/10.1016/j.procs.2022.09.190" target="_blank" >http://dx.doi.org/10.1016/j.procs.2022.09.190</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1016/j.procs.2022.09.190" target="_blank" >10.1016/j.procs.2022.09.190</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Human-Generated Web Data Disentanglement for Complex Event Processing

Popis výsledku v původním jazyce

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.

Název v anglickém jazyce

Human-Generated Web Data Disentanglement for Complex Event Processing

Popis výsledku anglicky

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.

Druh

D - Stať ve sborníku

CEP obor

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OECD FORD obor

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Projekt

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Návaznosti

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2022

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název statě ve sborníku

26th International Conference on Knowledge-Based and Intelligent Information and Engineering Systems, KES 2022

ISBN

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ISSN

1877-0509

e-ISSN

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Počet stran výsledku

9

Strana od-do

1341-1349

Název nakladatele

Elsevier

Místo vydání

Verona, Italy

Místo konání akce

Verona, Italy

Datum konání akce

1. 1. 2022

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

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