# Knowledge Graphs

535 papers with code • 2 benchmarks • 34 datasets

## Libraries

Use these libraries to find Knowledge Graphs models and implementations## Datasets

## Subtasks

## Most implemented papers

# Modeling Relational Data with Graph Convolutional Networks

We demonstrate the effectiveness of R-GCNs as a stand-alone model for entity classification.

# Open Graph Benchmark: Datasets for Machine Learning on Graphs

We present the Open Graph Benchmark (OGB), a diverse set of challenging and realistic benchmark datasets to facilitate scalable, robust, and reproducible graph machine learning (ML) research.

# Inductive Relation Prediction by Subgraph Reasoning

The dominant paradigm for relation prediction in knowledge graphs involves learning and operating on latent representations (i. e., embeddings) of entities and relations.

# Convolutional 2D Knowledge Graph Embeddings

In this work, we introduce ConvE, a multi-layer convolutional network model for link prediction, and report state-of-the-art results for several established datasets.

# KGAT: Knowledge Graph Attention Network for Recommendation

To provide more accurate, diverse, and explainable recommendation, it is compulsory to go beyond modeling user-item interactions and take side information into account.

# Learning Hierarchy-Aware Knowledge Graph Embeddings for Link Prediction

HAKE is inspired by the fact that concentric circles in the polar coordinate system can naturally reflect the hierarchy.

# Query2box: Reasoning over Knowledge Graphs in Vector Space using Box Embeddings

Our main insight is that queries can be embedded as boxes (i. e., hyper-rectangles), where a set of points inside the box corresponds to a set of answer entities of the query.

# TuckER: Tensor Factorization for Knowledge Graph Completion

Knowledge graphs are structured representations of real world facts.

# RotatE: Knowledge Graph Embedding by Relational Rotation in Complex Space

We study the problem of learning representations of entities and relations in knowledge graphs for predicting missing links.

# Knowledge-aware Graph Neural Networks with Label Smoothness Regularization for Recommender Systems

Here we propose Knowledge-aware Graph Neural Networks with Label Smoothness regularization (KGNN-LS) to provide better recommendations.