Knowledge Graphs

535 papers with code • 2 benchmarks • 34 datasets

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Use these libraries to find Knowledge Graphs models and implementations
4 papers
2 papers

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.

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.

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.

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

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.

TuckER: Tensor Factorization for Knowledge Graph Completion

ibalazevic/TuckER IJCNLP 2019

Knowledge graphs are structured representations of real world facts.

RotatE: Knowledge Graph Embedding by Relational Rotation in Complex Space

awslabs/dgl-ke ICLR 2019

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

hwwang55/KGNN-LS 11 May 2019

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