A Review of Relational Machine Learning for Knowledge Graphs

@article{Nickel2015ARO,
  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={2015},
  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…
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1,253 Citations
Highly Influential Citations
126
Background Citations
677
Methods Citations
266
Results Citations
7

1,253 Citations

Generalized Embedding Model for Knowledge Graph Mining

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.

Bridging Weighted Rules and Graph Random Walks for Statistical Relational Models

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

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

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.

Predicting the co-evolution of event and Knowledge Graphs

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

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

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.

Data-driven graph construction and graph learning: A review

Survey on graph embeddings and their applications to machine learning problems on graphs

This survey aims to describe the core concepts of graph embeddings and provide several taxonomies for their description, and presents an in-depth analysis of models based on network types, and overviews a wide range of applications to machine learning problems on graphs.

Survey on graph embeddings and their applications to machine learning problems on graphs

This survey covers a new rapidly growing family of automated graph feature engineering techniques, presents an in-depth analysis of models based on network types, and overviews a wide range of applications to machine learning problems on graphs.
...

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