Afficher la notice abrégée

Similarity Fuzzy Semantic Networks and Inference. An Application to Analysis of Radical Discourse in Twitter

dc.contributor.authorCastro Peña, Juan Luis
dc.contributor.authorFrancisco Aparicio, Manuel
dc.date.accessioned2024-12-16T17:05:49Z
dc.date.available2024-12-16T17:05:49Z
dc.date.issued2023-01
dc.identifier.citationCastro, J.L., Francisco, M. (2023). Similarity Fuzzy Semantic Networks and Inference. An Application to Analysis of Radical Discourse in Twitter. In: Rutkowski, L., Scherer, R., Korytkowski, M., Pedrycz, W., Tadeusiewicz, R., Zurada, J.M. (eds) Artificial Intelligence and Soft Computing. ICAISC 2022. Lecture Notes in Computer Science(), vol 13588. Springer, Cham. https://doi.org/10.1007/978-3-031-23492-7_15es_ES
dc.identifier.urihttps://hdl.handle.net/10481/98064
dc.description.abstractIn this paper we introduce a new Knowledge Representation model, the Similarity Fuzzy Semantic Networks. It is an extension of Fuzzy Semantic Networks that incorporates reasoning by similarity through a Similarity Inference Rule. Moreover, we show as it can be effectively applied to a trending and complex problem like the analysis of radical discourse in Twitter.es_ES
dc.description.sponsorshipJunta de Andalucia, projects P18-FR-5020 and A-HUM-250-UGR18es_ES
dc.description.sponsorshipEuropean Social Fund (ESF).es_ES
dc.description.sponsorshipFPI 2017 predoctoral programme, from the Spanish Ministry of Economy and Competitiveness (MINECO), grant reference BES- 2017-081202.es_ES
dc.language.isoenges_ES
dc.publisherSpringer Naturees_ES
dc.subjectFuzzy Semantic Networkses_ES
dc.subjectSimilarity Fuzzy Reasoninges_ES
dc.subjectSocial Network Analysises_ES
dc.subjectKnowledge Engineeringes_ES
dc.subjectSemantic Networkes_ES
dc.titleSimilarity Fuzzy Semantic Networks and Inference. An Application to Analysis of Radical Discourse in Twitteres_ES
dc.typebook partes_ES
dc.rights.accessRightsopen accesses_ES
dc.identifier.doi10.1007/978-3-031-23492-7_15
dc.type.hasVersionAMes_ES


Fichier(s) constituant ce document

[PDF]
Nom:
samplepaper.pdf
Taille:
257.2Ko
Format:
PDF
Description:
Manuscrito aceptado

Ce document figure dans la(les) collection(s) suivante(s)

Afficher la notice abrégée