https://bitbucket.org/sdumancic/embeddingsmeetilp
공식 코드A Comparative Study of Distributional and Symbolic Paradigms for Relational Learning
Papers with Code 아카이브
Dynamic graph embedding
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관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/Abigale001/FairDGE
공식 코드Toward Structure Fairness in Dynamic Graph Embedding: A Trend-aware Dual Debiasing Approach
https://github.com/GustikS/NeuraLifting
공식 코드Lossless Compression of Structured Convolutional Models via Lifting
https://github.com/GustikS/NeuraLogic
공식 코드Lossless Compression of Structured Convolutional Models via Lifting
https://github.com/JD-AI-Research-Silicon-Valley/SACN
공식 코드End-to-end Structure-Aware Convolutional Networks for Knowledge Base Completion
https://github.com/Mrlyk423/Relation_Extraction
공식 코드Modeling Relation Paths for Representation Learning of Knowledge Bases
https://github.com/RingBDStack/HAN-AT-based-KBC
공식 코드Modeling relation paths for knowledge base completion via joint adversarial training
https://github.com/Srinivas-R/AKBC-2017-Paper-14
공식 코드Revisiting Simple Neural Networks for Learning Representations of Knowledge Graphs
https://github.com/alanjohnvarghese/TransformerG2G
공식 코드TransformerG2G: Adaptive time-stepping for learning temporal graph embeddings using transformers
https://github.com/arthurcgusmao/XKE
공식 코드Interpreting Embedding Models of Knowledge Bases: A Pedagogical Approach
https://github.com/asappresearch/kbc-pomr
공식 코드Knowledge Base Completion for Constructing Problem-Oriented Medical Records
https://github.com/bio-ontology-research-group/DELE
공식 코드DELE: Deductive $\mathcal{EL}^{++} \thinspace $ Embeddings for Knowledge Base Completion
https://github.com/bio-ontology-research-group/cate
공식 코드Lattice-preserving $\mathcal{ALC}$ ontology embeddings with saturation
https://github.com/bio-ontology-research-group/falcon
공식 코드https://github.com/boschresearch/llm-vs-gibberish-ontologies
공식 코드Do LLMs Really Adapt to Domains? An Ontology Learning Perspective
https://github.com/brickee/zerokbc
공식 코드ZeroKBC: A Comprehensive Benchmark for Zero-Shot Knowledge Base Completion
https://github.com/bveseli/lmsforkbc
공식 코드https://github.com/cai-lw/KBGAN
공식 코드https://github.com/carolinlawrence/gradient-rollback
공식 코드Explaining Neural Matrix Factorization with Gradient Rollback
https://github.com/chenxran/instancebasedlearning
공식 코드https://github.com/daiquocnguyen/ConvKB
공식 코드A Novel Embedding Model for Knowledge Base Completion Based on Convolutional Neural Network
https://github.com/dair-iitd/kbi
공식 코드Joint Matrix-Tensor Factorization for Knowledge Base Inference
https://github.com/dair-iitd/kbi-regex
공식 코드https://github.com/dair-iitd/mokb6
공식 코드mOKB6: A Multilingual Open Knowledge Base Completion Benchmark
https://github.com/dair-iitd/tkbi
공식 코드Temporal Knowledge Base Completion: New Algorithms and Evaluation Protocols
https://github.com/datquocnguyen/STransE
공식 코드STransE: a novel embedding model of entities and relationships in knowledge bases
https://github.com/deepakn97/relationPrediction
공식 코드Learning Attention-based Embeddings for Relation Prediction in Knowledge Graphs
https://github.com/edwarddavis1/valid_conformal_for_dynamic_gnn
공식 코드https://github.com/facebookresearch/fastText
공식 코드https://github.com/facebookresearch/kbc
공식 코드Canonical Tensor Decomposition for Knowledge Base Completion
https://github.com/facebookresearch/ssl-relation-prediction
공식 코드https://github.com/facebookresearch/tkbc
공식 코드Tensor Decompositions for temporal knowledge base completion
https://github.com/fudancisl/freegem
공식 코드https://github.com/gracexu182/dyng2g
공식 코드DynG2G: An Efficient Stochastic Graph Embedding Method for Temporal Graphs
https://github.com/hmthanh/GCAT
공식 코드Graph Collaborative Attention Network for Link Prediction in Knowledge Graphs
https://github.com/hopkin-ghp/QuatDE
공식 코드QuatDE: Dynamic Quaternion Embedding for Knowledge Graph Completion
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.