https://github.com/daiquocnguyen/QuatRE
공식 코드QuatRE: Relation-Aware Quaternions for Knowledge Graph Embeddings
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/daiquocnguyen/R-MeN
공식 코드A Relational Memory-based Embedding Model for Triple Classification and Search Personalization
https://github.com/davidlamprecht/linkedpaperswithcode
공식 코드Linked Papers With Code: The Latest in Machine Learning as an RDF Knowledge Graph
https://github.com/davidlvxin/TransC
공식 코드Differentiating Concepts and Instances for Knowledge Graph Embedding
https://github.com/dchang56/snomed_kge
공식 코드Benchmark and Best Practices for Biomedical Knowledge Graph Embeddings
https://github.com/de-tum/gnce
공식 코드Cardinality Estimation over Knowledge Graphs with Embeddings and Graph Neural Networks
https://github.com/deepakn97/relationPrediction
공식 코드Learning Attention-based Embeddings for Relation Prediction in Knowledge Graphs
https://github.com/dfdazac/blp
공식 코드Inductive Entity Representations from Text via Link Prediction
https://github.com/dice-group/Convolutional-Complex-Knowledge-Graph-Embeddings
공식 코드https://github.com/dice-group/PYKE
공식 코드https://github.com/dice-group/dice-embeddings
공식 코드https://github.com/dice-group/universal_embeddings
공식 코드https://github.com/duyguislakoglu/temt
공식 코드https://github.com/dwslab/jRDF2Vec
공식 코드RDF2Vec Light -- A Lightweight Approach for Knowledge Graph Embeddings
https://github.com/facebookresearch/fastText
공식 코드https://github.com/facebookresearch/ssl-relation-prediction
공식 코드https://github.com/guilhermesfc/ontology-matching-absolute-orientation
공식 코드Ontology Matching Through Absolute Orientation of Embedding Spaces
https://github.com/guojiapub/bique
공식 코드https://github.com/hipgraph/sptransx
공식 코드https://github.com/hkust-knowcomp/complexhyperbolickge
공식 코드Complex Hyperbolic Knowledge Graph Embeddings with Fast Fourier Transform
https://github.com/honggen-zhang/hasa-ckge
공식 코드HaSa: Hardness and Structure-Aware Contrastive Knowledge Graph Embedding
https://github.com/hyleepp/continue-kgc
공식 코드https://github.com/i40-Tools/I40KG-Embeddings
공식 코드Unveiling Relations in the Industry 4.0 Standards Landscape based on Knowledge Graph Embeddings
https://github.com/ibalazevic/HypER
공식 코드https://github.com/ireklos/pathe
공식 코드PathE: Leveraging Entity-Agnostic Paths for Parameter-Efficient Knowledge Graph Embeddings
https://github.com/jayachaturvedi/pain_in_mental_health
공식 코드https://github.com/kang205/MoHR
공식 코드Recommendation Through Mixtures of Heterogeneous Item Relationships
https://github.com/kgrl2021/submission-one
공식 코드A Study on Knowledge Graph Embeddings and Graph Neural Networks for Web Of Things
https://github.com/lautel/meg
공식 코드MEG: Medical Knowledge-Augmented Large Language Models for Question Answering
https://github.com/lenazellinger/counterfactual_kgr
공식 코드https://github.com/lion-zs/gie
공식 코드https://github.com/liseda-lab/seek
공식 코드Explainable Representations for Relation Prediction in Knowledge Graphs
https://github.com/liseda-lab/truewalks
공식 코드Biomedical Knowledge Graph Embeddings with Negative Statements
https://github.com/m-hildebrandt/R2D2
공식 코드https://github.com/malllabiisc/InteractE
공식 코드InteractE: Improving Convolution-based Knowledge Graph Embeddings by Increasing Feature Interactions
https://github.com/malteos/pytorch-bert-document-classification
공식 코드Enriching BERT with Knowledge Graph Embeddings for Document Classification
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.