Awesome Knowledge Graph Embedding Approaches

awesome-kge.md

Awesome Knowledge Graph Embedding Approaches

This list contains repositories of libraries and approaches for knowledge graph embeddings, which are vector representations of entities and relations in a multi-relational directed labelled graph. Licensed under CC0.

Libraries

• 🆕 AmpliGraph (4 algorithms) @ https://github.com/Accenture/AmpliGraph
• Embedding framework (5 algorithms) @ https://github.com/BookmanHan/Embedding
• KB2EP collection (5 algorithms) @ https://github.com/thunlp/KB2EP
• 🆕 OpenKE (8 algorithms) @ https://github.com/thunlp/OpenKE
• 🆕 PyKEEN (10 algorithms) @ https://github.com/SmartDataAnalytics/PyKEEN/
• Python KGE (6 algorithms) @ https://github.com/mana-ysh/knowledge-graph-embeddings
• scikit-kge (3 algorithms) @ https://github.com/mnick/scikit-kge
• scikit-tensor (5 algorithms) @ https://github.com/mnick/scikit-tensor
• SME (4 algorithms) @ https://github.com/glorotxa/SME

Approaches

• ComplEx @ https://github.com/ttrouill/complex
• ComplEx-NNE-AER @ https://github.com/iieir-km/ComplEx-NNE_AER
• ConvE @ https://github.com/TimDettmers/ConvE
• EbemKG @ https://github.com/pminervini/ebemkg
• 🆕 fastText @ https://github.com/facebookresearch/fastText/tree/master/scripts/kbcompletion
• HolE @ https://github.com/mnick/holographic-embeddings
• Inferbeddings @ https://github.com/uclmr/inferbeddings
• KG2Vec @ https://github.com/AKSW/KG2Vec
• KGE-LDA @ https://github.com/yao8839836/KGE-LDA
• KR-EAR @ https://github.com/thunlp/KR-EAR
• LiteralE @ https://github.com/SmartDataAnalytics/LiteralE
• mFold @ https://github.com/v-shinc/mFoldEmbedding
• Neural-LP @ https://github.com/fanyangxyz/Neural-LP
• ProjE @ https://github.com/bxshi/ProjE
• RDF2Vec @ http://data.dws.informatik.uni-mannheim.de/rdf2vec/code/
• Resource2Vec @ https://github.com/AKSW/Resource2Vec/tree/master/resource2vec-core
• TranslatingModel @ https://github.com/ZichaoHuang/TranslatingModel
• wiki2vec (for DBpedia only) @ https://github.com/idio/wiki2vec

DGL-KE： https://github.com/awslabs/dgl-ke , including TransE, TransR, RESCAL, DistMult, ComplEx, and RotatE.

Hi,

CKB is a tool to make knowledge graph embeddings using HuggingFace models. This is useful when a knowledge graph contains text for example. DistillBert and FlauBERT are available. The source code is developed so that new HuggingFace models can be added without difficulty.

CKB works with MKB which is a more traditional tool for knowledge graph embeddings. MKB makes it easy to evaluate a model using link prediction / triplet classification.

CKB and MKB lack documentation compared to the libraries already referenced.