Knowledge Engineering and Data Mining Volume II

Dear Colleagues,

Extracting knowledge from data is a fundamental process in creating intelligent information retrieval systems, decision support, and knowledge management. Among the welcome topics of work, we seek research on data mining methods, multidimensional data analysis, supervised and unsupervised learning methods, methods of knowledge base management, language ontologies, ontology learning, and others. We encourage you to present new algorithms and work on practical solutions, i.e., applications/systems presenting the actually created applications of the proposed research achievements.

The Special Issue covers the entire knowledge engineering pipeline: from data acquisition and data mining to knowledge extraction and exploitation. For this reason, we have conceived this Special Issue, the purpose of which is to gather the many researchers operating in the field to contribute to a collective effort in understanding the trends and future questions in the field of knowledge engineering and data mining. Topics include, but are not limited to:

• knowledge acquisition and engineering;
• data mining methods;
• big knowledge analytics;
• data mining, knowledge discovery, and machine learning;
• knowledge modeling and processing;
• knowledge acquisition and engineering;
• query and natural language processing;
• data and information modeling;
• data and information semantics;
• data-intensive applications;
• knowledge representation and reasoning;
• decision support systems;
• decision-making;
• group decision-making;
• rules mining;
• outliers mining;
• data exploration;
• data science;
• semantic web data and linked data;
• ontologies and controlled vocabularies;
• data acquisition;
• multidimensional data analysis;
• supervised and unsupervised learning methods;
• parallel processing and modeling;
• languages based on parallel programming and data mining.

Dr. Agnieszka Konys
Prof. Dr. Agnieszka Nowak-Brzezińska
Guest Editors

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