# Knowledge Graphs

953 papers with code • 3 benchmarks • 41 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.

# 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.

# 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.

# 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.

# 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.

# Beta Embeddings for Multi-Hop Logical Reasoning in Knowledge Graphs

Logical operations are performed in the embedding space by neural operators over the probabilistic embeddings.

# 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.

# OGB-LSC: A Large-Scale Challenge for Machine Learning on Graphs

Enabling effective and efficient machine learning (ML) over large-scale graph data (e. g., graphs with billions of edges) can have a great impact on both industrial and scientific applications.