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Knowledge Base Completion

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Knowledge base completion is the task which automatically infers missing facts by reasoning about the information already present in the knowledge base. A knowledge base is a collection of relational facts, often represented in the form of "subject", "relation", "object"-triples.

코드

관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.

https://github.com/dmlc/dgl/tree/master/examples/pytorch/rgcn-hetero

Modeling Relational Data with Graph Convolutional Networks

https://github.com/dmlc/dgl/tree/master/examples/tensorflow/rgcn

Modeling Relational Data with Graph Convolutional Networks

https://github.com/giuseppefutia/semi

Modeling Relational Data with Graph Convolutional Networks

https://github.com/glorotxa/SME

Combining Two And Three-Way Embeddings Models for Link Prediction in Knowledge Bases

https://github.com/guillaumejaume/tuto-dl-on-graphs

Modeling Relational Data with Graph Convolutional Networks

https://github.com/hkiyomaru/pu-learning

Learning from positive and unlabeled data: a survey

https://github.com/kracr/document-level-relation-extraction

Modeling Relational Data with Graph Convolutional Networks

https://github.com/masakicktashiro/rgcn_pytorch_implementation

Modeling Relational Data with Graph Convolutional Networks

https://github.com/mniepert/mmkb

KBLRN : End-to-End Learning of Knowledge Base Representations with Latent, Relational, and Numerical Features

https://github.com/nec-research/dccg

Traversing Knowledge Graphs in Vector Space

https://github.com/parkererickson/crunchBaseGraph

Modeling Relational Data with Graph Convolutional Networks

https://github.com/pouyapez/multim-kb-embeddings

Embedding Multimodal Relational Data for Knowledge Base Completion

https://github.com/predict-idlab/RR-GCN

Modeling Relational Data with Graph Convolutional Networks

https://github.com/shijx12/kqapro_baselines

Modeling Relational Data with Graph Convolutional Networks

https://github.com/susurrant/flow-imputation

Modeling Relational Data with Graph Convolutional Networks

https://github.com/susurrant/spatial-interaction-modeling

Modeling Relational Data with Graph Convolutional Networks

https://github.com/thiviyanT/torch-rgcn

Modeling Relational Data with Graph Convolutional Networks

https://github.com/tkipf/gae

Modeling Relational Data with Graph Convolutional Networks

https://github.com/toooooodo/rgcn-linkprediction

Modeling Relational Data with Graph Convolutional Networks

https://github.com/twktheainur/kbc

Canonical Tensor Decomposition for Knowledge Base Completion

https://github.com/usherwang02/SemanticMatchingEnergy-Theano

Combining Two And Three-Way Embeddings Models for Link Prediction in Knowledge Bases

https://github.com/vincentschen/limited-label-scene-graphs

Scene Graph Prediction with Limited Labels

https://github.com/wliu88/AttentivePathRanking

Path Ranking with Attention to Type Hierarchies

https://github.com/yangyucheng000/Papers/tree/main/MindSpore-Retrieval-augmented

Modeling Relational Data with Graph Convolutional Networks

출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.