https://github.com/muhaochen/MTransE
Multilingual Knowledge Graph Embeddings for Cross-lingual Knowledge Alignment
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
Entity Alignment is the task of finding entities in two knowledge bases that refer to the same real-world object. It plays a vital role in automatically integrating multiple knowledge bases.
Note: results that have incorporated machine translated entity names (introduced in the RDGCN paper) or pre-alignment name embeddings are considered to have used extra training labels (both are marked with "Extra Training Data" in the leaderboard) and are not adhere to a comparable setting with others that have followed the original setting of the benchmark.
Source: Cross-lingual Entity Alignment via Joint Attribute-Preserving Embedding
The task of entity alignment is related to the task of entity resolution which focuses on matching structured entity descriptions in different contexts.
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
Multilingual Knowledge Graph Embeddings for Cross-lingual Knowledge Alignment
Multilingual Knowledge Graph Embeddings for Cross-lingual Knowledge Alignment
RAGA: Relation-aware Graph Attention Networks for Global Entity Alignment
Knowledge Graphs Meet Multi-Modal Learning: A Comprehensive Survey
Knowledge Graphs Meet Multi-Modal Learning: A Comprehensive Survey
Rethinking Uncertainly Missing and Ambiguous Visual Modality in Multi-Modal Entity Alignment
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.