NeuralKG
[Tool] For Knowledge Graph Representation Learning
파일 탐색기
최종 버전 다운로드 (.zip)- BoxE_FB15K237.yaml
- CompGCN_FB15K237.yaml
- ComplEx_FB15K237.yaml
- ComplEx_NNE_AER_FB15K.yaml
- ConvE_FB15K237.yaml
- CrossE_FB15K237.yaml
- DistMult_FB15K237.yaml
- DualE_FB15K237.yaml
- HAKE_FB15K237.yaml
- IterE_FB15k-237-sparse.yaml
- KBAT_FB15K237.yaml
- PairRE_FB15K237.yaml
- RGCN_FB15K237.yaml
- RotatE_FB15K237.yaml
- RugE_FB15K237.yaml
- SEGNN_FB15K237.yaml
- SimplE_FB15K237.yaml
- TransE_FB15K237.yaml
- TransH_FB15K237.yaml
- TransR_FB15K237.yaml
- XTransE_FB15K237.yaml
- BoxE_WN18RR.yaml
- CompGCN_WN18RR.yaml
- ComplEx_WN18RR.yaml
- ConvE_WN18RR.yaml
- CrossE_WN18RR.yaml
- DistMult_WN18RR.yaml
- DualE_WN18RR.yaml
- HAKE_WN18RR.yaml
- IterE_WN18RR-sparse.yaml
- KBAT_WN18RR.yaml
- PairRE_WN18RR.yaml
- RGCN_WN18RR.yaml
- RotatE_WN18RR.yaml
- SEGNN_WN18RR.yaml
- SimplE_WN18RR.yaml
- TransE_WN18RR.yaml
- TransH_WN18RR.yaml
- TransR_WN18RR.yaml
- XTransE_WN18RR.yaml
- TransE_demo_kg.yaml
- data-preprocess.py
- entities.dict
- entity2id.txt
- relation2id.txt
- relations.dict
- test.txt
- train-1.txt
- train.txt
- train1.txt
- valid.txt
- _cons.txt
- entities.dict
- FB15K_rule
- groudings.txt
- relations.dict
- test.txt
- train.txt
- valid.txt
- axiom_equivalent.txt
- axiom_inferenceChain1.txt
- axiom_inferenceChain2.txt
- axiom_inferenceChain3.txt
- axiom_inferenceChain4.txt
- axiom_inverse.txt
- axiom_reflexive.txt
- axiom_subProperty.txt
- axiom_symmetric.txt
- axiom_transitive.txt
- equivalent_entailments
- inferencechain1_entailments
- inferencechain2_entailments
- inferencechain3_entailments
- inferencechain4_entailments
- inverse_entailments
- reflexive_entailments
- subproperty_entailments
- symmetric_entailments
- transitive_entailments
- valid_entailments.pickle
- _cons.txt
- entities.dict
- entity2id.txt
- relation2id.txt
- relations.dict
- test.txt
- train.txt
- valid.txt
- entities.dict
- README.txt
- relations.dict
- test.txt
- train.txt
- valid.txt
- _cons.txt
- entities.dict
- README
- relations.dict
- test.txt
- train.txt
- valid.txt
- Wordnet3.0-LICENSE
- entities.dict
- relations.dict
- test.txt
- train.txt
- valid.txt
- axiom_equivalent.txt
- axiom_inferenceChain1.txt
- axiom_inferenceChain2.txt
- axiom_inferenceChain3.txt
- axiom_inferenceChain4.txt
- axiom_inverse.txt
- axiom_reflexive.txt
- axiom_subProperty.txt
- axiom_symmetric.txt
- axiom_transitive.txt
- equivalent_entailments
- inferencechain1_entailments
- inferencechain2_entailments
- inferencechain3_entailments
- inferencechain4_entailments
- inverse_entailments
- reflexive_entailments
- subproperty_entailments
- symmetric_entailments
- transitive_entailments
- valid_entailments.pickle
- entities.dict
- entity2id.txt
- relation2id.txt
- relations.dict
- test.txt
- train.txt
- valid.txt
- environment.pickle
- gnnmodel.doctree
- index.doctree
- install.doctree
- kgemodel.doctree
- modules.doctree
- neuralkg.data.doctree
- neuralkg.doctree
- neuralkg.eval_task.doctree
- neuralkg.lit_model.doctree
- neuralkg.loss.doctree
- neuralkg.model.doctree
- result.doctree
- rulemodel.doctree
- base_data_module.html
- DataPreprocess.html
- Grounding.html
- KGDataModule.html
- RuleDataLoader.html
- Sampler.html
- RGCN_prediction.html
- BaseLitModel.html
- CompGCNLitModel.html
- ConvELitModel.html
- CrossELitModel.html
- IterELitModel.html
- KBATLitModel.html
- KGELitModel.html
- RGCNLitModel.html
- RugELitModel.html
- XTransELitModel.html
- Adv_Loss.html
- ComplEx_NNE_AER_Loss.html
- Cross_Entropy_Loss.html
- CrossE_Loss.html
- IterE_Loss.html
- KBAT_Loss.html
- Margin_Loss.html
- RGCN_Loss.html
- RugE_Loss.html
- SimplE_Loss.html
- CompGCN.html
- KBAT.html
- RGCN.html
- XTransE.html
- BoxE.html
- ComplEx.html
- ConvE.html
- CrossE.html
- DistMult.html
- model.html
- RotatE.html
- SimplE.html
- TransE.html
- TransH.html
- TransR.html
- ComplEx_NNE_AER.html
- IterE.html
- RugE.html
- index.html
- gnnmodel.rst.txt
- index.rst.txt
- install.rst.txt
- kgemodel.rst.txt
- modules.rst.txt
- neuralkg.data.rst.txt
- neuralkg.eval_task.rst.txt
- neuralkg.lit_model.rst.txt
- neuralkg.loss.rst.txt
- neuralkg.model.rst.txt
- neuralkg.rst.txt
- result.md.txt
- rulemodel.rst.txt
- fontawesome-webfont.eot
- fontawesome-webfont.svg
- fontawesome-webfont.ttf
- fontawesome-webfont.woff
- fontawesome-webfont.woff2
- lato-bold-italic.woff
- lato-bold-italic.woff2
- lato-bold.woff
- lato-bold.woff2
- lato-normal-italic.woff
- lato-normal-italic.woff2
- lato-normal.woff
- lato-normal.woff2
- Roboto-Slab-Bold.woff
- Roboto-Slab-Bold.woff2
- Roboto-Slab-Regular.woff
- Roboto-Slab-Regular.woff2
- badge_only.css
- custom.css
- theme.css
- badge_only.js
- html5shiv-printshiv.min.js
- html5shiv.min.js
- theme.js
- basic.css
- doctools.js
- documentation_options.js
- file.png
- jquery-3.5.1.js
- jquery.js
- language_data.js
- minus.png
- plus.png
- pygments.css
- searchtools.js
- underscore-1.13.1.js
- underscore.js
- .buildinfo
- .nojekyll
- genindex.html
- gnnmodel.html
- index.html
- install.html
- kgemodel.html
- modules.html
- neuralkg.data.html
- neuralkg.eval_task.html
- neuralkg.html
- neuralkg.lit_model.html
- neuralkg.loss.html
- neuralkg.model.html
- objects.inv
- py-modindex.html
- result.html
- rulemodel.html
- search.html
- searchindex.js
- custom.css
- conf.py
- gnnmodel.rst
- index.rst
- install.rst
- kgemodel.rst
- modules.rst
- neuralkg.data.rst
- neuralkg.eval_task.rst
- neuralkg.lit_model.rst
- neuralkg.loss.rst
- neuralkg.model.rst
- neuralkg.rst
- result.md
- rulemodel.rst
- make.bat
- Makefile
- demo.gif
- logo.png
- neuralkg-ind2.png
- neuralkg2.png
- overview.png
- axiom_pools.pickle
- axiom_prob.pickle
- BoxE_FB.sh
- CompGCN_FB.sh
- ComplEx_FB.sh
- ConvE_FB.sh
- CrossE_FB.sh
- DistMult_FB.sh
- DualE_FB.sh
- HAKE_FB.sh
- IterE_FB.sh
- KBAT_FB.sh
- NNE_FB.sh
- PairRE_FB.sh
- RGCN_FB.sh
- RotatE_FB.sh
- RugE_FB.sh
- SEGNN_FB.sh
- SimplE_FB.sh
- TransE_FB.sh
- TransH_FB.sh
- TransR_FB.sh
- XTransE_FB.sh
- BoxE_WN.sh
- CompGCN_WN.sh
- ComplEx_WN.sh
- ConvE_WN.sh
- CrossE_WN.sh
- DistMult_WN.sh
- DualE_WN.sh
- HAKE_WN.sh
- IterE_WN.sh
- KBAT_WN.sh
- PairRE_WN.sh
- RGCN_WN.sh
- RotatE_WN.sh
- SEGNN_WN.sh
- SimplE_WN.sh
- TransE_WN.sh
- TransH_WN.sh
- TransR_WN.sh
- XTransE_WN.sh
- __init__.py
- base_data_module.py
- DataPreprocess.py
- Grounding.py
- KGDataModule.py
- RuleDataLoader.py
- Sampler.py
- __init__.py
- link_prediction.py
- link_prediction_SEGNN.py
- __init__.py
- BaseLitModel.py
- CompGCNLitModel.py
- ConvELitModel.py
- CrossELitModel.py
- IterELitModel.py
- KBATLitModel.py
- KGELitModel.py
- RGCNLitModel.py
- RugELitModel.py
- SEGNNLitModel.py
- XTransELitModel.py
- __init__.py
- Adv_Loss.py
- ComplEx_NNE_AER_Loss.py
- Cross_Entropy_Loss.py
- CrossE_Loss.py
- KBAT_Loss.py
- Margin_Loss.py
- RGCN_Loss.py
- RugE_Loss.py
- SimplE_Loss.py
- Softplus_Loss.py
- __init__.py
- CompGCN.py
- KBAT.py
- RGCN.py
- SEGNN.py
- XTransE.py
- __init__.py
- BoxE.py
- ComplEx.py
- ConvE.py
- CrossE.py
- DistMult.py
- DualE.py
- HAKE.py
- model.py
- PairRE.py
- RotatE.py
- SimplE.py
- TransE.py
- TransH.py
- TransR.py
- __init__.py
- ComplEx_NNE_AER.py
- IterE.py
- model.py
- RugE.py
- __init__.py
- __init__.py
- setup_parser.py
- tools.py
- __init__.py
- .gitignore
- demo.py
- LICENSE
- main.py
- README.md
- README_CN.md
- requirements.txt
- setup.py
# 설치 가이드
1. 코드 내려받기
git clone https://github.com/zjukg/NeuralKG
깃허브에서 프로젝트 코드 전체를 내 컴퓨터로 내려받습니다.
cd NeuralKG
방금 내려받은 프로젝트 폴더 안으로 이동합니다.
2. 공식 설치 스크립트
쉬움 추천사전 준비물
- Python 3 pip 명령어를 쓰려면 Python이 필요합니다.
pip install torch==1.9.1+cu111 -f https://download.pytorch.org/whl/torch_stable.html
PyPI에 배포된 패키지를 바로 설치합니다. 소스 클론이 필요 없습니다.
pip install dgl-cu111 dglgo -f https://data.dgl.ai/wheels/repo.html
PyPI에 배포된 패키지를 바로 설치합니다. 소스 클론이 필요 없습니다.
pip install neuralkg
PyPI에 배포된 패키지를 바로 설치합니다. 소스 클론이 필요 없습니다.
설치 후 새 터미널을 열고, 프로그램의 버전 확인 명령(예: --version)으로 정상 설치됐는지 확인하세요.
이 레포의 README에 적힌 실제 명령어를 그대로 가져왔습니다.
3. Python
쉬움사전 준비물
pip install torch==1.9.1+cu111 -f https://download.pytorch.org/whl/torch_stable.html
PyPI에 배포된 패키지를 바로 설치합니다. 소스 클론이 필요 없습니다.
pip install dgl-cu111 dglgo -f https://data.dgl.ai/wheels/repo.html
PyPI에 배포된 패키지를 바로 설치합니다. 소스 클론이 필요 없습니다.
pip install neuralkg
PyPI에 배포된 패키지를 바로 설치합니다. 소스 클론이 필요 없습니다.
에러 메시지 없이 실행되고 터미널에 안내 문구가 출력되면 정상입니다.
이 레포의 README에 적힌 실제 명령어를 그대로 가져왔습니다.
// repository documentation
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