Knowledge Graphs

743 papers with code • 3 benchmarks • 37 datasets

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Use these libraries to find Knowledge Graphs models and implementations
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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.

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

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.

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.

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.

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.

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.

Embedding Logical Queries on Knowledge Graphs

williamleif/graphqembed NeurIPS 2018

Learning low-dimensional embeddings of knowledge graphs is a powerful approach used to predict unobserved or missing edges between entities.