743 papers with code • 3 benchmarks • 37 datasets
These leaderboards are used to track progress in Knowledge Graphs
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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.
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.
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.
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.
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.
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.
Embedding Logical Queries on Knowledge Graphs
Learning low-dimensional embeddings of knowledge graphs is a powerful approach used to predict unobserved or missing edges between entities.