https://github.com/knowlab/bi-weekly-paper-presentation
Understanding the Performance of Knowledge Graph Embeddings in Drug Discovery
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
Graph embeddings learn a mapping from a network to a vector space, while preserving relevant network properties.
<span style="color:grey; opacity: 0.6">( Image credit: GAT )</span>
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
Understanding the Performance of Knowledge Graph Embeddings in Drug Discovery
Attributed Graph Clustering: A Deep Attentional Embedding Approach
DHGE: Dual-View Hyper-Relational Knowledge Graph Embedding for Link Prediction and Entity Typing
Predicting Gene-Disease Associations with Knowledge Graph Embeddings over Multiple Ontologies
SimKGC: Simple Contrastive Knowledge Graph Completion with Pre-trained Language Models
HittER: Hierarchical Transformers for Knowledge Graph Embeddings
RotatE: Knowledge Graph Embedding by Relational Rotation in Complex Space
Learning Hierarchy-Aware Knowledge Graph Embeddings for Link Prediction
Answering Visual-Relational Queries in Web-Extracted Knowledge Graphs
Multilingual Knowledge Graph Embeddings for Cross-lingual Knowledge Alignment
Multilingual Knowledge Graph Embeddings for Cross-lingual Knowledge Alignment
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.