TY - BOOK ID - 133465680 TI - Knowledge Modelling and Learning through Cognitive Networks AU - Stella, Massimo AU - Kenett, Yoed N. PY - 2022 PB - Basel MDPI - Multidisciplinary Digital Publishing Institute DB - UniCat KW - Information technology industries KW - text mining KW - big data KW - analytics KW - review KW - self-organization KW - computational philosophy KW - brain KW - synaptic learning KW - adaptation KW - functional plasticity KW - activity-dependent resonance states KW - circular causality KW - somatosensory representation KW - prehensile synergies KW - robotics KW - COVID-19 KW - social media KW - hashtag networks KW - emotional profiling KW - cognitive science KW - network science KW - sentiment analysis KW - computational social science KW - Twitter KW - VADER scoring KW - correlation KW - semantic network analysis KW - intellectual disability KW - adolescents KW - EEG KW - emotional states KW - working memory KW - depression KW - anxiety KW - graph theory KW - classification KW - machine learning KW - neural networks KW - phonotactic probability KW - neighborhood density KW - sub-lexical representations KW - lexical representations KW - phonemes KW - biphones KW - cognitive network KW - smart assistants KW - knowledge generation KW - intelligent systems KW - web components KW - deep learning KW - web-based interaction KW - cognitive network science KW - text analysis KW - natural language processing KW - artificial intelligence KW - emotional recall KW - cognitive data KW - AI KW - pharmacological text corpus KW - automatic relation extraction KW - gender stereotypes KW - story tropes KW - movie plots KW - network analysis KW - word co-occurrence network KW - n/a UR - https://www.unicat.be/uniCat?func=search&query=sysid:133465680 AB - 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 how memory recall patterns influence phenomena such as creativity, memory search, learning, and more generally, knowledge acquisition, exploration, and exploitation. In parallel, neural network models for artificial intelligence (AI) are also becoming more widespread as inferential models for understanding which features drive language-related phenomena such as meaning reconstruction, stance detection, and emotional profiling. Whereas cognitive networks map explicitly which entities engage in associative relationships, neural networks perform an implicit mapping of correlations in cognitive data as weights, obtained after training over labelled data and whose interpretation is not immediately evident to the experimenter. This book aims to bring together quantitative, innovative research that focuses on modelling knowledge through cognitive and neural networks to gain insight into mechanisms driving cognitive processes related to knowledge structuring, exploration, and learning. The book comprises a variety of publication types, including reviews and theoretical papers, empirical research, computational modelling, and big data analysis. All papers here share a commonality: they demonstrate how the application of network science and AI can extend and broaden cognitive science in ways that traditional approaches cannot. ER -