Data Integration
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Data integration (also called information integration) is the process of consolidating data from a set of heterogeneous data sources into a single uniform data set (materialized integration) or view on the data (virtual integration). Data integration pipelines involve subtasks such as schema matching, table annotation, entity resolution, value normalization, data cleansing, and data fusion. Application domains of data integration include data warehousing, data lakes, and knowledge base consolidation. Surveys on Data integration:
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/wbsg-uni-mannheim/ALMSER-GB
https://github.com/wbsg-uni-mannheim/EntityMatchingTaskProfiler
https://github.com/wbsg-uni-mannheim/jointbert
https://github.com/wbsg-uni-mannheim/productbert-intermediate
https://github.com/wbsg-uni-mannheim/wdc-sotab
https://github.com/weyoun2211/productbert-intermediate
https://github.com/yangyucheng000/University/tree/main/model-1/rag
RAGA: Relation-aware Graph Attention Networks for Global Entity Alignment
https://github.com/zjukg/maco
Knowledge Graphs Meet Multi-Modal Learning: A Comprehensive Survey
https://github.com/zjukg/mygo
Knowledge Graphs Meet Multi-Modal Learning: A Comprehensive Survey
https://github.com/zjukg/umaea
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.