A Review of Relational Machine Learning for Knowledge Graphs

Relational machine learning studies methods for the statistical analysis of relational, or graph-structured, data. In this paper, we provide a review of how such statistical models can be "trained" on large knowledge graphs, and then used to predict new facts about the world (which is equi...

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Vydané v:	Proceedings of the IEEE Ročník 104; číslo 1; s. 11 - 33
Hlavní autori:	Nickel, Maximilian, Murphy, Kevin, Tresp, Volker, Gabrilovich, Evgeniy
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	New York IEEE 01.01.2016 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Predmet:	Big data Biological system modeling Computational efficiency Computer graphs Data mining Equivalence Factorization Graph-based models Graphs Knowledge based systems knowledge extraction knowledge graphs latent feature models Machine learning Mathematical models Neural networks Predictive models Resource description framework Statistical analysis statistical relational learning statistical relational learning Graph-based models knowledge graphs latent feature models knowledge extraction
ISSN:	0018-9219, 1558-2256
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