A Review of Relational Machine Learning for Knowledge Graphs
Maximilian Nickel,Kevin Murphy,Volker Tresp,Evgeniy Gabrilovich +3 more
- 01 Jan 2016
- Vol. 104, Iss: 1, pp 11-33
TL;DR: This paper provides a review of how 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) and how such statistical models of graphs can be combined with text-based information extraction methods for automatically constructing knowledge graphs from the Web.
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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.
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Citations
Link prediction based on the mutual information with high-order clustering structure of nodes in complex networks
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- 01 Dec 2022
TL;DR: In this paper , a link prediction approach based on Mutual information of the High-Order Clustering structure (MHOC) is proposed, which integrates the effects of multiple higher order structures of nodes and quantifies the different contributions of common neighbors with the aid of the diverse high-order clustering coefficients of nodes based on information entropy for predicting missing links.
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