# A Review of Relational Machine Learning for Knowledge Graphs

@article{Nickel2016ARO, title={A Review of Relational Machine Learning for Knowledge Graphs}, author={Maximilian Nickel and Kevin P. Murphy and Volker Tresp and Evgeniy Gabrilovich}, journal={Proceedings of the IEEE}, year={2016}, volume={104}, pages={11-33} }

Relational machine learning studies methods for the statistical analysis of relational, or graph-structured, data. [] Key Method 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…

## 1,224 Citations

### Generalized Embedding Model for Knowledge Graph Mining

- Computer Science
- 2018

This paper conjecture that the one-shot supervised learning mechanism is a bottleneck in improving the performance of the graph embedding learning, and proposes to extend this by introducing a multi-shot "unsupervised" learning framework where a 2-layer MLP network for every shot.

### Determining the Number of Latent Factors in Statistical Multi-Relational Learning

- Computer ScienceJ. Mach. Learn. Res.
- 2019

The focus of this paper is to determine the number of latent factors in the RESCAL model, and design a specific pseudometric, prove the consistency of the MLEs under this pseudometric and establish its rate of convergence.

### Bridging Weighted Rules and Graph Random Walks for Statistical Relational Models

- Computer ScienceFront. Robot. AI
- 2018

This article provides a simple way to normalize relations and proves that relational logistic regression using normalized relations generalizes the path ranking algorithm, which provides a better understanding of relational learning.

### Large-scale Machine Learning over Graphs

- Computer Science
- 2017

An online algorithm for multi-task learning with provable sublinear regret bound is developed, where a latent graph of task interdependencies is dynamically inferred on-the-fly, and a new approach to impose analogical structures among heterogeneous nodes is proposed.

### RA-GCN: Relational Aggregation Graph Convolutional Network for Knowledge Graph Completion

- Computer ScienceICMLC
- 2020

This paper finds that a subset of the set of entities may be directly connected to a central entity and these similar attributes and relationships can be abstractly aggregated into virtual entities and virtual relationships, respectively, to better extract the topological relationship features.

### Type-Constrained Representation Learning in Knowledge Graphs

- Computer ScienceSEMWEB
- 2015

This work integrated prior knowledge in form of type-constraints in various state of the art latent variable approaches and shows that prior knowledge on relation-types significantly improves these models up to 77% in link-prediction tasks.

### Predicting the co-evolution of event and Knowledge Graphs

- Computer Science2016 19th International Conference on Information Fusion (FUSION)
- 2016

This paper introduces an additional set of tensors that contain temporal information that will be used to predict the events that will happen in future time steps, using for that task both dynamic information from the previous event tensors and static information that is stored in the knowledge graph.

### Analysis of the Impact of Negative Sampling on Link Prediction in Knowledge Graphs

- Computer ScienceArXiv
- 2017

This paper uses state-of-the-art knowledge graph embeddings -- \rescal, TransE, DistMult and ComplEX -- and evaluates on benchmark datasets -- FB15k and WN18, and proposes embedding based sampling methods.

### Complex-Valued Embedding Models for Knowledge Graphs

- Computer Science
- 2017

An experimentals survey of state-of-the-art factorization models, not towards a purely comparative end, but as a means to get insight about their inductive abilities, and proposes new researchdirections to improve on existing models, including ComplEx.

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