Inductive Relation Prediction by Subgraph Reasoning

Published in

International Conference on Machine Learning(2019)

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TL;DR

A graph neural network based relation prediction framework, GraIL, that reasons over local subgraph structures and has a strong inductive bias to learn entity-independent relational semantics is proposed.

Abstract

The dominant paradigm for relation prediction in knowledge graphs involves learning and operating on latent representations (i.e., embeddings) of entities and relations. However, these embedding-based methods do not explicitly capture the compositional logical rules underlying the knowledge graph, and they are limited to the transductive setting, where the full set of entities must be known during training. Here, we propose a graph neural network based relation prediction framework, GraIL, that reasons over local subgraph structures and has a strong inductive bias to learn entity-independent relational semantics. Unlike embedding-based models, GraIL is naturally inductive and can generalize to unseen entities and graphs after training. We provide theoretical proof and strong empirical evidence that GraIL can represent a useful subset of first-order logic and show that GraIL outperforms existing rule-induction baselines in the inductive setting. We also demonstrate significant gains obtained by ensembling GraIL with various knowledge graph embedding methods in the transductive setting, highlighting the complementary inductive bias of our method.

Authors

William L. Hamilton

9 papers

Komal K. Teru

2 papers

E. Denis

1 papers

References37 items

Efficient Probabilistic Logic Reasoning with Graph Neural Networks

DRUM: End-To-End Differentiable Rule Mining On Knowledge Graphs

CLUTRR: A Diagnostic Benchmark for Inductive Reasoning from Text

Probabilistic Logic Neural Networks for Reasoning

Inductive Graph Pattern Learning for Recommender Systems Based on a Graph Neural Network

Inductive Relation Prediction by Subgraph Reasoning

Published in

International Conference on Machine Learning(2019)

External Links:

Generate Graph

TL;DR

Abstract

Authors

William L. Hamilton

9 papers

Komal K. Teru

2 papers

E. Denis

1 papers

TL;DR

Abstract

Authors

References37 items

Efficient Probabilistic Logic Reasoning with Graph Neural Networks

DRUM: End-To-End Differentiable Rule Mining On Knowledge Graphs

CLUTRR: A Diagnostic Benchmark for Inductive Reasoning from Text

Probabilistic Logic Neural Networks for Reasoning

Inductive Graph Pattern Learning for Recommender Systems Based on a Graph Neural Network

TL;DR

Abstract

Authors

References37 items

Efficient Probabilistic Logic Reasoning with Graph Neural Networks

DRUM: End-To-End Differentiable Rule Mining On Knowledge Graphs

CLUTRR: A Diagnostic Benchmark for Inductive Reasoning from Text

Probabilistic Logic Neural Networks for Reasoning

Inductive Graph Pattern Learning for Recommender Systems Based on a Graph Neural Network

Logic Attention Based Neighborhood Aggregation for Inductive Knowledge Graph Embedding

Fine-Grained Evaluation of Rule- and Embedding-Based Systems for Knowledge Graph Completion

How Powerful are Graph Neural Networks?

RotatE: Knowledge Graph Embedding by Relational Rotation in Complex Space

Representation Learning on Graphs with Jumping Knowledge Networks

Link Prediction Based on Graph Neural Networks

Representation Learning on Graphs: Methods and Applications

DeepPath: A Reinforcement Learning Method for Knowledge Graph Reasoning

Deep Gaussian Embedding of Attributed Graphs: Unsupervised Inductive Learning via Ranking

Convolutional 2D Knowledge Graph Embeddings

Knowledge Transfer for Out-of-Knowledge-Base Entities : A Graph Neural Network Approach

Inductive Representation Learning on Large Graphs

Neural Message Passing for Quantum Chemistry

Modeling Relational Data with Graph Convolutional Networks

Differentiable Learning of Logical Rules for Knowledge Base Reasoning

Knowledge Graph Completion via Complex Tensor Factorization

Geometric Deep Learning: Going beyond Euclidean data

TensorLog: A Differentiable Deductive Database

Revisiting Semi-Supervised Learning with Graph Embeddings

Gated Graph Sequence Neural Networks

A Review of Relational Machine Learning for Knowledge Graphs

Embedding Entities and Relations for Learning and Inference in Knowledge Bases

Translating Embeddings for Modeling Multi-relational Data

AMIE: association rule mining under incomplete evidence in ontological knowledge bases

An optimal lower bound on the number of variables for graph identification

Approximation capabilities of multilayer feedforward networks

An optimal lower bound on the number of variables for graph identification

Lemma

Inductive Relation Prediction by Subgraph Reasoning

Logical Expressiveness of Graph Neural Networks

Representing Text for Joint Embedding of Text and Knowledge Bases

The Graph Neural Network Model

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