https://github.com/xincanfeng/dimension
공식 코드Sharing Parameter by Conjugation for Knowledge Graph Embeddings in Complex Space
Papers with Code 아카이브
Triple Classification
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Triple classification aims to judge whether a given triple (h, r, t) is correct or not with respect to the knowledge graph.
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/xwhan/DeepPath
공식 코드DeepPath: A Reinforcement Learning Method for Knowledge Graph Reasoning
https://github.com/yao8839836/kg-bert
공식 코드https://github.com/yao8839836/kg-llm
공식 코드Exploring Large Language Models for Knowledge Graph Completion
https://github.com/yihuazhu111/orthogonale
공식 코드Block-Diagonal Orthogonal Relation and Matrix Entity for Knowledge Graph Embedding
https://github.com/yuhebai/sem-kge
공식 코드A Semantic Partitioning Method for Large-Scale Training of Knowledge Graph Embeddings
https://github.com/yunchengwang/greenkgc
공식 코드https://github.com/yur7nd/ptss
공식 코드Repurposing Knowledge Graph Embeddings for Triple Representation via Weak Supervision
https://github.com/zjukg/kgtransformer
공식 코드Structure Pretraining and Prompt Tuning for Knowledge Graph Transfer
https://github.com/zjunlp/PromptKG/tree/main/deltaKG
공식 코드https://github.com/zjunlp/knn-kg
공식 코드Reasoning Through Memorization: Nearest Neighbor Knowledge Graph Embeddings
https://github.com/zjunlp/promptkg
공식 코드LambdaKG: A Library for Pre-trained Language Model-Based Knowledge Graph Embeddings
https://gitlab.com/chen00217/bert_subsumption
공식 코드Contextual Semantic Embeddings for Ontology Subsumption Prediction
https://gitlab.com/patryk.preisner/mkga
공식 코드Universal Preprocessing Operators for Embedding Knowledge Graphs with Literals
https://worksheets.codalab.org/worksheets/0xc757f29f5c794e5eb7bfa8ca9c945573
공식 코드Learning Symmetric Collaborative Dialogue Agents with Dynamic Knowledge Graph Embeddings
https://github.com/Alishaba/pykeen-snapshot-ensembles
Multiple Run Ensemble Learning with Low-Dimensional Knowledge Graph Embeddings
https://github.com/HazyResearch/KGEmb
https://github.com/INK-USC/RE-Net
https://github.com/JohnKurian/R2D2
https://github.com/KXY-PUBLIC/NASE
Learning Attention-based Embeddings for Relation Prediction in Knowledge Graphs
https://github.com/LB0828/conve_reproduce
https://github.com/MIRALab-USTC/KGE-DURA
Learning Hierarchy-Aware Knowledge Graph Embeddings for Link Prediction
https://github.com/ManasRMohanty/DS5500-capstone
https://github.com/SmartDataAnalytics/PyKEEN
PyKEEN 1.0: A Python Library for Training and Evaluating Knowledge Graph Embeddings
https://github.com/WeidongLi-KG/KBGAN_PyTorch-v0.4.1
https://github.com/abhinavnagpal/KNOWLEDGE-GRAPH-PAPERS
Seq2RDF: An end-to-end application for deriving Triples from Natural Language Text
https://github.com/adymaharana/DeepPath_PyTorch
DeepPath: A Reinforcement Learning Method for Knowledge Graph Reasoning
https://github.com/alexrenz/adaps
NuPS: A Parameter Server for Machine Learning with Non-Uniform Parameter Access
https://github.com/ant-research/KnowledgeGraphEmbeddingsViaPairedRelationVectors_PairRE
PairRE: Knowledge Graph Embeddings via Paired Relation Vectors
https://github.com/bi-graph/emgraph
https://github.com/cdhx/KBGAN_for_RotatE
https://github.com/cheungdaven/QuatE
https://github.com/dice-group/hybridfc
https://github.com/dodo47/SpikE
Learning through structure: towards deep neuromorphic knowledge graph embeddings
https://github.com/guixiangyu1/KGE-HAKE-master
Learning Hierarchy-Aware Knowledge Graph Embeddings for Link Prediction
https://github.com/gychant/CSKMTermDefn
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.