https://github.com/HarryShomer/Better-Inductive-KGC
공식 코드Towards Better Benchmark Datasets for Inductive Knowledge Graph Completion
Papers with Code 아카이브
Inductive knowledge graph completion
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코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/IBM/RuDaS
공식 코드RuDaS: Synthetic Datasets for Rule Learning and Evaluation Tools
https://github.com/THU-KEG/KEPLER
공식 코드KEPLER: A Unified Model for Knowledge Embedding and Pre-trained Language Representation
https://github.com/anilakash/indkgc
공식 코드Inductive Knowledge Graph Completion with GNNs and Rules: An Analysis
https://github.com/bdi-lab/ingram
공식 코드InGram: Inductive Knowledge Graph Embedding via Relation Graphs
https://github.com/dfdazac/blp
공식 코드Inductive Entity Representations from Text via Link Prediction
https://github.com/idea-finai/cats
공식 코드https://github.com/irokin/GPFL
공식 코드Towards Learning Instantiated Logical Rules from Knowledge Graphs
https://github.com/irokin/RuleHierarchy
공식 코드Building Rule Hierarchies for Efficient Logical Rule Learning from Knowledge Graphs
https://github.com/kkteru/grail
공식 코드https://github.com/xiaomingaaa/sdn
공식 코드S$^2$DN: Learning to Denoise Unconvincing Knowledge for Inductive Knowledge Graph Completion
https://github.com/zjukg/rmpi
공식 코드Relational Message Passing for Fully Inductive Knowledge Graph Completion
https://github.com/alisadeghian/DRUM
DRUM: End-To-End Differentiable Rule Mining On Knowledge Graphs
https://github.com/automl-research/red-gnn
https://github.com/canlinzhang/siailp
https://github.com/jaemuzzin/plagnn
https://github.com/lars-research/red-gnn
https://github.com/migalkin/NodePiece
https://github.com/migalkin/NodePiece/tree/main/ogb
https://github.com/raphaelsty/mkb
Inductive Entity Representations from Text via Link Prediction
https://github.com/tgebhart/sheaf_kg_transind
https://github.com/woodcutter1998/ogb-grail-mod
https://github.com/zjukg/morse
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.