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

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URI: https://hdl.handle.net/10481/98064
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Auteur
Castro Peña, Juan Luis; Francisco Aparicio, Manuel
Editorial
Springer Nature
Materia
Fuzzy Semantic Networks
 
Similarity Fuzzy Reasoning
 
Social Network Analysis
 
Knowledge Engineering
 
Semantic Network
 
Date
2023-01
Referencia bibliográfica
Castro, 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_15
Patrocinador
Junta de Andalucia, projects P18-FR-5020 and A-HUM-250-UGR18; European Social Fund (ESF).; FPI 2017 predoctoral programme, from the Spanish Ministry of Economy and Competitiveness (MINECO), grant reference BES- 2017-081202.
Résumé
In 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.
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