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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Veröffentlicht in:Proceedings of the IEEE Jg. 104; H. 1; S. 11 - 33
Hauptverfasser: Nickel, Maximilian, Murphy, Kevin, Tresp, Volker, Gabrilovich, Evgeniy
Format: Journal Article
Sprache:Englisch
Veröffentlicht: New York IEEE 01.01.2016
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:
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
Online-Zugang:Volltext
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Zusammenfassung: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 equivalent to predicting new edges in the graph). In particular, we discuss two fundamentally different kinds of statistical relational models, both of which can scale to massive data sets. The first is based on latent feature models such as tensor factorization and multiway neural networks. The second is based on mining observable patterns in the graph. We also show how to combine these latent and observable models to get improved modeling power at decreased computational cost. Finally, we discuss how such statistical models of graphs can be combined with text-based information extraction methods for automatically constructing knowledge graphs from the Web. To this end, we also discuss Google's knowledge vault project as an example of such combination.
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ISSN:0018-9219
1558-2256
DOI:10.1109/JPROC.2015.2483592