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.
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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
Originální 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 1877-0509
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 firank_B
Změnil Změnil: RNDr. Pavel Šmerk, Ph.D., učo 3880. Změněno: 6. 4. 2023 07:36.
Anotace
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.
VytisknoutZobrazeno: 23. 7. 2024 08:17