Exploiting Semantic Web Knowledge Graphs in Data Mining

Autor/en:
Petar Ristoski
Umfang:
244
EAN/ISBN:
978-3-89838-742-2
Erscheinungsdatum:
Monday, 01 July 2019
Band:
038
Ausgabe:
Softcover
Buchreihe:
Studies on the Semantic Web
Categories:
Buch
Semantic Web
Studies on the Semantic Web
Gesamtverzeichnis AKA Verlag#Complete Index AKA Publisher
Semantic Technology
Availability: lieferbar
Preis:
incl. 7% MWSt
60,00 €

Data Mining and Knowledge Discovery in Databases (KDD) is a research field concerned with deriving higher-level insights from data. The tasks performed in this field are knowledge intensive and can benefit from additional knowledge from various sources, so many approaches have been proposed that combine Semantic Web data with the data mining and knowledge discovery process.

This book, Exploiting Semantic Web Knowledge Graphs in Data Mining, aims to show that Semantic Web knowledge graphs are useful for generating valuable data mining features that can be used in various data mining tasks. In Part I, Mining Semantic Web Knowledge Graphs, the author evaluates unsupervised feature generation strategies from types and relations in knowledge graphs used in different data mining tasks such as classification, regression, and outlier detection. Part II, Semantic Web Knowledge Graphs Embeddings, proposes an approach that circumvents the shortcomings introduced with the approaches in Part I, developing an approach that is able to embed complete Semantic Web knowledge graphs in a low dimensional feature space where each entity and relation in the knowledge graph is represented as a numerical vector. Finally, Part III, Applications of Semantic Web Knowledge Graphs, describes a list of applications that exploit Semantic Web knowledge graphs like classification and regression, showing that the approaches developed in Part I and Part II can be used in applications in various domains.

The book will be of interest to all those working in the field of data mining and KDD.