Knowledge Graph Embedding
92 papers with code • 1 benchmarks • 2 datasets
Benchmarks
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FB15k
Greatest papers with code
CoKE: Contextualized Knowledge Graph Embedding
This work presents Contextualized Knowledge Graph Embedding (CoKE), a novel paradigm that takes into account such contextual nature, and learns dynamic, flexible, and fully contextualized entity and relation embeddings.
Learning Embeddings from Knowledge Graphs With Numeric Edge Attributes
Numeric values associated to edges of a knowledge graph have been used to represent uncertainty, edge importance, and even out-of-band knowledge in a growing number of scenarios, ranging from genetic data to social networks.
Probability Calibration for Knowledge Graph Embedding Models
Accenture/AmpliGraph
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ICLR 2020
We show popular embedding models are indeed uncalibrated.
Calibration for Link Prediction
Knowledge Graph Embedding
+1
A Survey on Knowledge Graphs: Representation, Acquisition and Applications
shaoxiongji/awesome-knowledge-graph
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•
In this survey, we provide a comprehensive review of knowledge graph covering overall research topics about 1) knowledge graph representation learning, 2) knowledge acquisition and completion, 3) temporal knowledge graph, and 4) knowledge-aware applications, and summarize recent breakthroughs and perspective directions to facilitate future research.
GraphVite: A High-Performance CPU-GPU Hybrid System for Node Embedding
DeepGraphLearning/graphvite
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•
In this paper, we propose GraphVite, a high-performance CPU-GPU hybrid system for training node embeddings, by co-optimizing the algorithm and the system.
Ranked #1 on
Node Classification
on YouTube
RotatE: Knowledge Graph Embedding by Relational Rotation in Complex Space
DeepGraphLearning/KnowledgeGraphEmbedding
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ICLR 2019
We study the problem of learning representations of entities and relations in knowledge graphs for predicting missing links.
Ranked #2 on
Link Prediction
on FB122
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.
Ranked #1 on
Link Prediction
on WN18
(training time (s) metric)
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.
The KEEN Universe: An Ecosystem for Knowledge Graph Embeddings with a Focus on Reproducibility and Transferability
pykeen/pykeen
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Therefore, we present the KEEN Universe, an ecosystem for knowledge graph embeddings that we have developed with a strong focus on reproducibility and transferability.
LibKGE - A knowledge graph embedding library for reproducible research
LibKGE ( https://github. com/uma-pi1/kge ) is an open-source PyTorch-based library for training, hyperparameter optimization, and evaluation of knowledge graph embedding models for link prediction.