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The task becomes more challenging on temporal knowledge graphs, where each fact is associated with a timestamp.
We release an open toolkit for knowledge embedding (OpenKE), which provides a unified framework and various fundamental models to embed knowledge graphs into a continuous low-dimensional space.
In statistical relational learning, knowledge graph completion deals with automatically understanding the structure of large knowledge graphs---labeled directed graphs---and predicting missing relationships---labeled edges.
Ranked #2 on Knowledge Graphs on FB15k
Learning embeddings of entities and relations is an efficient and versatile method to perform machine learning on relational data such as knowledge graphs.
Ranked #6 on Link Prediction on FB15k
We demonstrate the effectiveness of R-GCNs as a stand-alone model for entity classification.
Ranked #1 on Node Classification on AIFB
Neural language representation models such as BERT pre-trained on large-scale corpora can well capture rich semantic patterns from plain text, and be fine-tuned to consistently improve the performance of various NLP tasks.
Ranked #1 on Entity Linking on FIGER
We show that the expressive models significantly outperform simple scalable baselines, indicating an opportunity for dedicated efforts to further improve graph ML at scale.
Ranked #1 on Node Classification on MAG240M-LSC
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.