Knowledge Engineering and Data Mining

doi:10.3390/books978-3-0365-6789-1

Download Url(s)

https://mdpi.com/books/pdfview/book/6944

Contributor(s)

Konys, Agnieszka (editor)

Nowak-Brzezińska, Agnieszka (editor)

Language

English

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Abstract

Knowledge engineering and data mining are fundamental topics in the area of artificial intelligence and knowledge-based systems. This Special Issue covers the entire knowledge engineering pipeline: from data acquisition and data mining to knowledge extraction and exploitation. The reader will find topics including data mining methods, multidimensional data analysis, supervised and unsupervised learning methods, methods of knowledge-based management, language ontologies, ontology learning, and others.

URI

https://directory.doabooks.org/handle/20.500.12854/98891

Keywords

computing-intensive data; dynamic programming; loop nest tiling; parallel code; OpenMP C/C++; credit scoring; cash loans; machine learning; decision model; classification; feature selection; resampling; discretization; knowledge representation; formal ontologies; graph databases; evaluation feature selection; evaluation model; psychosocial education; recommendation systems; rank aggregation; differential evolution; supervised learning; matrix factorization; metaheuristic; clinical named entity recognition; Chinese medical text; pre-trained model; systematic review; multicriteria; MCDA; MCDM; MADM; MODM; AHP; TOPSIS; VIKOR; PROMETHEE; ANP; computer-aided design (CAD); educational data mining; engineering education; online and hybrid learning environments; social media analytics; soft tissue; gamma correction; landmark detection; X-ray images; facial profile; prediction; artificial neural network; support vector machine; random forest; regression; offshore wave; wind speed; unmanned aerial vehicle; UAV smoke show; mobile networks; artificial intelligence; healthcare; database design; geospatial data; software; knowledge management; reasoning; information extraction; rule mining; knowledge acquisition; engineering; ontology; knowledge base; sustainable supplier selection; ontology population; knowledge acquisition from text; knowledge mining; drug–drug interaction; graph convolutional network; self-attention; deep learning; n/a