Knowledge Graphs

953 papers with code • 3 benchmarks • 41 datasets

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Libraries

Use these libraries to find Knowledge Graphs models and implementations
4 papers
2,079
3 papers
1,935
3 papers
37
2 papers
588
See all 5 libraries.

Most implemented papers

Modeling Relational Data with Graph Convolutional Networks

tkipf/relational-gcn 17 Mar 2017

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

Open Graph Benchmark: Datasets for Machine Learning on Graphs

snap-stanford/ogb NeurIPS 2020

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

DeepGraphLearning/KnowledgeGraphEmbedding ICLR 2019

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

Inductive Relation Prediction by Subgraph Reasoning

kkteru/grail ICML 2020

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

MIRALab-USTC/KGE-HAKE 21 Nov 2019

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

Convolutional 2D Knowledge Graph Embeddings

TimDettmers/ConvE 5 Jul 2017

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

xiangwang1223/knowledge_graph_attention_network 20 May 2019

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

snap-stanford/KGReasoning NeurIPS 2020

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

hyren/query2box ICLR 2020

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

snap-stanford/ogb 17 Mar 2021

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.