Knowledge Modelling and Learning through Cognitive Networks

• Andere auteurs: Stella, Massimo (Redacteur), Kenett, Yoed N. (Redacteur)
• Formaat: Elektronisch Hoofdstuk
• Taal: Engels
• Gepubliceerd in: Basel MDPI - Multidisciplinary Digital Publishing Institute 2022
• Onderwerpen: Information technology industries; text mining; big data; analytics; review; self-organization; computational philosophy; brain; synaptic learning; adaptation; functional plasticity; activity-dependent resonance states; circular causality; somatosensory representation; prehensile synergies; robotics; COVID-19; social media; hashtag networks; emotional profiling; cognitive science; network science; sentiment analysis; computational social science; Twitter; VADER scoring; correlation; semantic network analysis; intellectual disability; adolescents; EEG; emotional states; working memory; depression; anxiety; graph theory; classification; machine learning; neural networks; phonotactic probability; neighborhood density; sub-lexical representations; lexical representations; phonemes; biphones; cognitive network; smart assistants; knowledge generation; intelligent systems; web components; deep learning; web-based interaction; cognitive network science; text analysis; natural language processing; artificial intelligence; emotional recall; cognitive data; AI; pharmacological text corpus; automatic relation extraction; gender stereotypes; story tropes; movie plots; network analysis; word co-occurrence network; n/a
• Online toegang: DOAB: download the publication; DOAB: description of the publication

One of the most promising developments in modelling knowledge is cognitive network science, which aims to investigate cognitive phenomena driven by the networked, associative organization of knowledge. For example, investigating the structure of semantic memory via semantic networks has illuminated...