Knowledge Graph Embeddings
109 papers with code • 0 benchmarks • 4 datasets
Benchmarks
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Libraries
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MutualFriends
OLPBENCH
Most implemented papers
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.
NuPS: A Parameter Server for Machine Learning with Non-Uniform Parameter Access
alexrenz/NuPS
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Parameter servers (PSs) facilitate the implementation of distributed training for large machine learning tasks.
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.
KGE-CL: Contrastive Learning of Tensor Decomposition Based Knowledge Graph Embeddings
wentao-xu/kge-cl
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COLING 2022
Learning the embeddings of knowledge graphs (KG) is vital in artificial intelligence, and can benefit various downstream applications, such as recommendation and question answering.
Contextual Semantic Embeddings for Ontology Subsumption Prediction
Automating ontology construction and curation is an important but challenging task in knowledge engineering and artificial intelligence.
Adversarial Robustness of Representation Learning for Knowledge Graphs
perubhardwaj/inferenceattack
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This thesis argues that state-of-the-art KGE models are vulnerable to data poisoning attacks, that is, their predictive performance can be degraded by systematically crafted perturbations to the training knowledge graph.
LambdaKG: A Library for Pre-trained Language Model-Based Knowledge Graph Embeddings
zjunlp/promptkg
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Knowledge Graphs (KGs) often have two characteristics: heterogeneous graph structure and text-rich entity/relation information.
Editing Language Model-based Knowledge Graph Embeddings
zjunlp/promptkg
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To address this issue, we propose a new task of editing language model-based KG embeddings in this paper.
RDF-star2Vec: RDF-star Graph Embeddings for Data Mining
Knowledge Graphs (KGs) such as Resource Description Framework (RDF) data represent relationships between various entities through the structure of triples (<subject, predicate, object>).
Analysis of the Impact of Negative Sampling on Link Prediction in Knowledge Graphs
bhushank/kge-rl
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We note a marked difference in the impact of these sampling methods on the two datasets, with the "traditional" corrupting positives method leading to best results on WN18, while embedding based methods benefiting the task on FB15k.
