Knowledge Graph Embedding
197 papers with code • 1 benchmarks • 4 datasets
Libraries
Use these libraries to find Knowledge Graph Embedding models and implementations
FB15k
KG20C
Most implemented papers
Temporal Knowledge Graph Embedding Model based on Additive Time Series Decomposition
soledad921/ATISE
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Moreover, considering the temporal uncertainty during the evolution of entity/relation representations over time, we map the representations of temporal KGs into the space of multi-dimensional Gaussian distributions.
Probability Calibration for Knowledge Graph Embedding Models
Accenture/AmpliGraph
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ICLR 2020
We show popular embedding models are indeed uncalibrated.
You CAN Teach an Old Dog New Tricks! On Training Knowledge Graph Embeddings
uma-pi1/kge
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ICLR 2020
A vast number of KGE techniques for multi-relational link prediction have been proposed in the recent literature, often with state-of-the-art performance.
Bringing Light Into the Dark: A Large-scale Evaluation of Knowledge Graph Embedding Models Under a Unified Framework
The heterogeneity in recently published knowledge graph embedding models' implementations, training, and evaluation has made fair and thorough comparisons difficult.
Multi-Partition Embedding Interaction with Block Term Format for Knowledge Graph Completion
Knowledge graph embedding methods perform this task by representing entities and relations as embedding vectors and modeling their interactions to compute the matching score of each triple.
Jointly Non-Sampling Learning for Knowledge Graph Enhanced Recommendation
However, existing KG enhanced recommendation methods have largely focused on exploring advanced neural network architectures to better investigate the structural information of KG.
PyKEEN 1.0: A Python Library for Training and Evaluating Knowledge Graph Embeddings
pykeen/pykeen
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Recently, knowledge graph embeddings (KGEs) received significant attention, and several software libraries have been developed for training and evaluating KGEs.
TeRo: A Time-aware Knowledge Graph Embedding via Temporal Rotation
soledad921/ATISE
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COLING 2020
We show our proposed model overcomes the limitations of the existing KG embedding models and TKG embedding models and has the ability of learning and inferringvarious relation patterns over time.
FedE: Embedding Knowledge Graphs in Federated Setting
AnselCmy/FedE
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Knowledge graphs (KGs) consisting of triples are always incomplete, so it's important to do Knowledge Graph Completion (KGC) by predicting missing triples.
Understanding the Performance of Knowledge Graph Embeddings in Drug Discovery
astrazeneca/kgem-in-drug-discovery
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Knowledge Graphs (KG) and associated Knowledge Graph Embedding (KGE) models have recently begun to be explored in the context of drug discovery and have the potential to assist in key challenges such as target identification.
