# A Review of Relational Machine Learning for Knowledge Graphs

Source: [A Review of Relational Machine Learning for Knowledge Graphs](<https://www.sota2.com/research/paper/68fa62ed1e209131ce9fbe1d>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-25T20:29:18.716Z

Authors: Maximilian Nickel, Kevin Murphy, Volker Tresp, Evgeniy Gabrilovich

Published: 2015-03-02T21:35:41.000Z

## 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.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

- [Temporal Link Prediction on YAGO15K filtered (test)](<https://www.sota2.com/research/sota/temporal-link-prediction-on-yago15k-filtered-test.md>): Temporal Link Prediction; YAGO15K filtered (test); MRR: 29.6.

- [Temporal Link Prediction on ICEWS14 filtered (test)](<https://www.sota2.com/research/sota/temporal-link-prediction-on-icews14-filtered-test.md>): Temporal Link Prediction; ICEWS14 filtered (test); MRR: 28.

- [Temporal Link Prediction on ICEWS Interpolation 05-15 (test)](<https://www.sota2.com/research/sota/temporal-link-prediction-on-icews-interpolation-05-15-test.md>): Temporal Link Prediction; ICEWS Interpolation 05-15 (test); Hits@1: 8.4.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/1503.00759>)

[PDF](<https://arxiv.org/pdf/1503.00759.pdf>)