Semantic Web Technology and Recommender Systems

Edited by
April 2023
284 pages
  • ISBN978-3-0365-7210-9 (Hardback)
  • ISBN978-3-0365-7211-6 (PDF)

This book is a reprint of the Special Issue Semantic Web Technology and Recommender Systems that was published in

Computer Science & Mathematics

In this book (Volume I), 13 papers have been published on different topics of the wide research areas of Semantic Web and Recommender systems. These papers have been carefully selected based on the peer review of several respectful reviewers organized by MDPI’s BDCC journal. This issue has attracted well-known international research teams, who we would like to thank for their work.

  • Hardback
License and Copyright
© 2022 by the authors; CC BY-NC-ND license
keyword search; RDF; interactive information retrieval; named entity disambiguation; text annotation; word sense disambiguation; ontologies; Wikification; neural networks; machine learning; digital parliament; digital transformation; legal tech; disruptive technologies; technology framework; parliamentary administrators; ParlTech; knowledge-driven processes; parliamentary hype cycle; semantic web; linked data; spatial datasets; data catalog; dataset recommender; semantic trajectories; recommender systems; big data analytics; user experience; cultural space; domain knowledge graph; natural language query; recommendation system; blockchain; cryptocurrency; Kendall Correlation Coefficient; Pearson Correlation Coefficient; sentiment analysis; social media analytics; Spearman Correlation Coefficient; Twitter; user influence; social media analysis; cultural spaces; cultural informatics; trending topics; SBVR; resource efficiency; fact type; operational rules; informal document to SBVR; natural language; recommendation systems; job matching; ontologies; reasoning; migrants; refugees; Context-Aware Recommender System; personalization; preferences; user modeling; journey planning; mobility; cold start; classification; clustering; ontology; semantic web; social data; terrorism; OWL/RDF; knowledge graphs; process mining; associative mining; personalization; recommendation; decision support

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