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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/AnselCmy/FedE
공식 코드https://github.com/ArchitParnami/Node2KG
공식 코드Transformation of Node to Knowledge Graph Embeddings for Faster Link Prediction in Social Networks
https://github.com/AynurGuluzade/DARLING
공식 코드Demographic Aware Probabilistic Medical Knowledge Graph Embeddings of Electronic Medical Records
https://github.com/DominikFilipiak/KGTN-ens
공식 코드KGTN-ens: Few-Shot Image Classification with Knowledge Graph Ensembles
https://github.com/HELL-TO-HEAVEN/KGE-CL
공식 코드KGE-CL: Contrastive Learning of Tensor Decomposition Based Knowledge Graph Embeddings
https://github.com/HKUST-KnowComp/FKGE
공식 코드https://github.com/KRR-Oxford/DeepOnto
공식 코드Contextual Semantic Embeddings for Ontology Subsumption Prediction
https://github.com/KokiKishimoto/cp_decomposition
공식 코드https://github.com/KokiKishimoto/cp_decomposition_alpha
공식 코드https://github.com/LivNLP/Relational-Walk-for-Knowledge-Graphs
공식 코드RelWalk A Latent Variable Model Approach to Knowledge Graph Embedding
https://github.com/MIRALab-USTC/KGE-HAKE
공식 코드Learning Hierarchy-Aware Knowledge Graph Embeddings for Link Prediction
https://github.com/NIVA-Knowledge-Graph/TERA
공식 코드Prediction of Adverse Biological Effects of Chemicals Using Knowledge Graph Embeddings
https://github.com/Pzoom522/ProcrustEs-KGE
공식 코드Highly Efficient Knowledge Graph Embedding Learning with Orthogonal Procrustes Analysis
https://github.com/SmartDataAnalytics/LiteralE
공식 코드https://github.com/SoyeonTiffanyMin/TransINT
공식 코드https://github.com/THU-KEG/KEPLER
공식 코드KEPLER: A Unified Model for Knowledge Embedding and Pre-trained Language Representation
https://github.com/TimDettmers/ConvE
공식 코드https://github.com/YueLiu/NeuralTripleTranslation
공식 코드Seq2RDF: An end-to-end application for deriving Triples from Natural Language Text
https://github.com/adaruna3/continual-kge
공식 코드https://github.com/aistairc/RDF-star2Vec
공식 코드https://github.com/aistairc/kgrc-rdf-star
공식 코드https://github.com/alexanderimanicowenrivers/Neural-Variational-Knowledge-Graphs
공식 코드Neural Variational Inference For Estimating Uncertainty in Knowledge Graph Embeddings
https://github.com/alexrenz/NuPS
공식 코드NuPS: A Parameter Server for Machine Learning with Non-Uniform Parameter Access
https://github.com/alipay/KnowledgeGraphEmbeddingsViaPairedRelationVectors_PairRE
공식 코드PairRE: Knowledge Graph Embeddings via Paired Relation Vectors
https://github.com/amberyzheng/bloomgml
공식 코드BloomGML: Graph Machine Learning through the Lens of Bilevel Optimization
https://github.com/andreeaiana/rdf2vec-materialization
공식 코드https://github.com/april-tools/gekcs
공식 코드How to Turn Your Knowledge Graph Embeddings into Generative Models
https://github.com/armitakhn/gilbert
공식 코드https://github.com/aryopg/biokge
공식 코드https://github.com/astrazeneca/biomedical-kg-topological-imbalance
공식 코드Implications of Topological Imbalance for Representation Learning on Biomedical Knowledge Graphs
https://github.com/astrazeneca/kgem-in-drug-discovery
공식 코드Understanding the Performance of Knowledge Graph Embeddings in Drug Discovery
https://github.com/bhushank/kge-rl
공식 코드Analysis of the Impact of Negative Sampling on Link Prediction in Knowledge Graphs
https://github.com/cai-lw/KBGAN
공식 코드https://github.com/catarina-canastra/benchmarking-knowledge-graph-embeddings-for-gene-disease-association-prediction
공식 코드A Systematic Evaluation of Knowledge Graph Embeddings for Gene-Disease Association Prediction
https://github.com/cynricfu/fedhgn
공식 코드FedHGN: A Federated Framework for Heterogeneous Graph Neural Networks
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.