A Review of Relational Machine Learning for Knowledge Graphs

TL;DR

This paper reviews statistical relational models for knowledge graphs, focusing on scalable latent feature and observable pattern-based methods, and explores their integration with text extraction for automatic knowledge graph construction.

Contribution

It provides a comprehensive overview of scalable relational models, compares latent and observable approaches, and discusses their combination with text-based extraction methods.

Findings

Latent feature models like tensor factorization scale well to large datasets.

Observable pattern mining offers an alternative scalable approach.

Combining models enhances prediction accuracy and efficiency.

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 datasets. 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.