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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Vydáno v:	Proceedings of the IEEE Ročník 104; číslo 1; s. 11 - 33
Hlavní autoři:	Nickel, Maximilian, Murphy, Kevin, Tresp, Volker, Gabrilovich, Evgeniy
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	New York IEEE 01.01.2016 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Témata:	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
On-line přístup:	Získat plný text
Tagy:	Přidat tag Žádné tagy, Buďte první, kdo vytvoří štítek k tomuto záznamu!

Abstract	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.
AbstractList	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.
Author	Tresp, Volker Gabrilovich, Evgeniy Murphy, Kevin Nickel, Maximilian
Author_xml	– sequence: 1 givenname: Maximilian surname: Nickel fullname: Nickel, Maximilian organization: Laboratory for Computational and Statistical Learning (LCSL), Massachusetts Institute of Technology, Cambridge, MA, USA – sequence: 2 givenname: Kevin surname: Murphy fullname: Murphy, Kevin organization: Google Inc., Mountain View, CA, USA – sequence: 3 givenname: Volker surname: Tresp fullname: Tresp, Volker organization: Siemens AG, Corporate Technology, Munich, Germany – sequence: 4 givenname: Evgeniy surname: Gabrilovich fullname: Gabrilovich, Evgeniy organization: Google Inc., Mountain View, CA, USA
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Snippet	Relational machine learning studies methods for the statistical analysis of relational, or graph-structured, data. In this paper, we provide a review of how...
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SubjectTerms	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
Title	A Review of Relational Machine Learning for Knowledge Graphs
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