TensorFlow implementation of TransE and its extended models for Knowledge Representation Learning
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Updated
Aug 24, 2018 - Python
TensorFlow implementation of TransE and its extended models for Knowledge Representation Learning
STransE: a novel embedding model of entities and relationships in knowledge bases (NAACL 2016)
Reproduceing the models of Knowledge Representation Learning (KRL), such as TransE, TransH etc.
A simple implement of TransE, the ML algorithm published in 2013
A TensorFlow-based implementation of knowledge graph embedding models.
It is the java implantation of paper "Translating Embeddings for Modeling Multi-relational Data".
Tongji Univ. xLab Knowledge Reasoning 2019
TransE implementation in Spark (pyspark)
Knowledge-Aware Graph Prompt Tuning for Cross-Domain Recommendation
Knowledge graph models manipulator which allows to perform various evaluations of provided embedders
Assignment on graph neural networks (GCN, GAT, Graph Auto-Encoder) and knowledge-graph embeddings (TransE) with DGL and TorchKGE
End-to-end knowledge graph for Explainable AI research. Property graphs (Neo4j), RDF/OWL reasoning, KG embeddings (PyKEEN), and GraphRAG vs vanilla RAG benchmark on 3,907 arXiv papers.
ARGA,ARGVA,AttentiveFP,Captum,ComplEx,CorrectAndSmooth,DeepGCNLayer,DeepGraphInfomax,DimeNet,DimeNetPlusPlus,DistMult,EdgeCNN,GAE,GAT,GATv2,GCN,GITMol,GLEM,GIN,GNNFF,GraphSAGE,GraphUNet,GRetriever,HeteroJumpingKnowledge,InnerProductDecoder,JumpingKnowledge,KGEModel,LabelPropagation,LightGCN,LINKX,MetaLayer,MetaPath2Vec,MLP,MoleculeGPT,Node2Vec,
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