Column Type Annotation
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Column type annotation (CTA) refers to the task of predicting the semantic type of a table column and is a subtask of Table Annotation. The labels that are usually used in a CTA problem are semantic types from vocabularies like DBpedia, Schema.org or WikiData. Some examples are: Book, Country, LocalBusiness etc.
CTA can be either treated as a multi-class classification problem where a column is annotated by only one semantic type or as multi-label classification problem where a column can be annotated using multiple semantic types.
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/Wyb0627/KBLink
공식 코드KGLink: A column type annotation method that combines knowledge graph and pre-trained language model
https://github.com/alan-turing-institute/SemAIDA
공식 코드ColNet: Embedding the Semantics of Web Tables for Column Type Prediction
https://github.com/megagonlabs/doduo
공식 코드https://github.com/megagonlabs/sato
공식 코드https://github.com/mitmedialab/sherlock-project
공식 코드Sherlock: A Deep Learning Approach to Semantic Data Type Detection
https://github.com/mkyl/chorus
공식 코드CHORUS: Foundation Models for Unified Data Discovery and Exploration
https://github.com/penfever/archetype
공식 코드ArcheType: A Novel Framework for Open-Source Column Type Annotation using Large Language Models
https://github.com/sunlab-osu/TURL
공식 코드https://github.com/wbsg-uni-mannheim/tabanngpt
공식 코드https://github.com/whnhch/accio
공식 코드ACCIO: Table Understanding Enhanced via Contrastive Learning with Aggregations
https://github.com/IBCNServices/Magic
https://github.com/awslabs/hypergraph-tabular-lm
https://github.com/fusion-jena/jentab
https://github.com/jiofidelus/tsotsa
https://github.com/pidahbus/DCoM
Sherlock: A Deep Learning Approach to Semantic Data Type Detection
https://github.com/wbsg-uni-mannheim/wdc-sotab
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.