{"url":"/sota/column-type-annotation-on-biodivtab","task":{"name":"Column Type Annotation","url":"/task/column-type-annotation","note":null},"dataset":{"name":"BiodivTab","url":"/dataset/biodivtab"},"category":"Natural Language Processing","categories":["Knowledge Base","Natural Language Processing"],"category_note":null,"description":"**Column type annotation** (CTA) refers to the task of predicting the semantic type of a table column and is a subtask of [Table Annotation](https://paperswithcode.com/task/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.\r\n\r\nCTA 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.","description_from":"task","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","rank":"the archive's row order at snapshot; not re-ranked","rows_end_at":"2025-07-28","rows_withheld_as_spam":0,"metric_values":"the archive's strings, untouched"},"metrics":["F1 (%)"],"metric_direction":{"note":"inferred from the metric name only (the archive records no direction); null = not inferred, chart draws points only","by_metric":{"F1 (%)":"higher"}},"counts":{"rows":6,"rows_with_code":3,"rows_with_paper_page":6,"rows_dated":6,"rows_using_additional_data":0},"rows":[{"rank_in_archive_order":1,"model":"KGCODE-Tab","metrics":{"F1 (%)":"86.7"},"uses_additional_data":false,"paper_date":"2022-10-25","paper":"/paper/kgcode-tab-results-for-semtab-2022","paper_url":"https://ceur-ws.org/Vol-3320/paper5.pdf","paper_title":"KGCODE-Tab Results for SemTab 2022","code":null,"n_code_links":0,"syntology":null},{"rank_in_archive_order":2,"model":"TSOTSA","metrics":{"F1 (%)":"76"},"uses_additional_data":false,"paper_date":"2022-10-25","paper":"/paper/towards-an-approach-based-on-knowledge-graph","paper_url":"https://ceur-ws.org/Vol-3320/paper12.pdf","paper_title":"Towards an Approach based on Knowledge Graph Refinement for Tabular Data to Knowledge Graph Matching","code":"https://github.com/jiofidelus/tsotsa","n_code_links":1,"syntology":null},{"rank_in_archive_order":3,"model":"Kepler-aSI","metrics":{"F1 (%)":"59.3"},"uses_additional_data":false,"paper_date":"2021-10-01","paper":"/paper/kepler-asi-at-semtab-2021","paper_url":"https://www.semanticscholar.org/paper/Kepler-aSI-at-SemTab-2021-Baazouzi-Kachroudi/f4ef58ea481fc2dbcc57a97886150a9c33e17840","paper_title":"Kepler-aSI at SemTab 2021","code":null,"n_code_links":0,"syntology":null},{"rank_in_archive_order":4,"model":"DAGOBAH","metrics":{"F1 (%)":"34.4"},"uses_additional_data":false,"paper_date":"2021-10-01","paper":"/paper/dagobah-table-and-graph-contexts-for","paper_url":"https://www.eurecom.fr/fr/publication/6842","paper_title":"DAGOBAH: Table and Graph Contexts for Efficient Semantic Annotation of Tabular Data","code":null,"n_code_links":0,"syntology":null},{"rank_in_archive_order":5,"model":"MAGIC","metrics":{"F1 (%)":"14.2"},"uses_additional_data":false,"paper_date":"2021-10-01","paper":"/paper/magic-mining-an-augmented-graph-using-ink","paper_url":"https://www.semanticscholar.org/paper/MAGIC%3A-Mining-an-Augmented-Graph-using-INK%2C-from-a-Steenwinckel-Turck/03465d28e575ac8273887f0f56b67b890230d788","paper_title":"MAGIC: Mining an Augmented Graph using INK, starting from a CSV","code":"https://github.com/IBCNServices/Magic","n_code_links":1,"syntology":null},{"rank_in_archive_order":6,"model":"JenTab","metrics":{"F1 (%)":"10.7"},"uses_additional_data":false,"paper_date":"2021-10-01","paper":"/paper/jentab-meets-semtab-2021-s-new-challenges","paper_url":"https://www.semanticscholar.org/paper/JenTab-Meets-SemTab-2021's-New-Challenges-Abdelmageed-Schindler/4f492fee6a7ae51d3f2527d9036a1beaf6f1e44b","paper_title":"JenTab Meets SemTab 2021's New Challenges","code":"https://github.com/fusion-jena/jentab","n_code_links":1,"syntology":null}],"since_archive":{"present":false,"note":"No Syntology-extracted rows are published in this build."},"syntology":{"read_at":"2026-09-24T18:15:14+00:00","claim":"Per row: N of M harvested code samples from that row's paper executed on a synthesized fixture; the other M-N are unverified. Not a reproduction of the row's number; not a correctness claim. n_pointer_only_licence counts samples the site points at rather than redistributes (a licence axis, independent of ran/unverified).","rows_with_graph_line":0,"rows_with_any_sample_ran":0,"distinct_papers_with_graph_line":0,"distinct_papers_with_any_sample_ran":0,"samples_over_distinct_papers":{"n_ran":0,"n_unverified":0,"n_samples":0,"n_pointer_only_licence":0,"note":"each paper (arXiv id) counted once, however many rows it is behind; this is the page-level figure"},"samples_row_weighted":{"n_ran":0,"n_unverified":0,"n_samples":0,"n_pointer_only_licence":0,"note":"row-weighted: a paper behind several rows is counted once per row; inflated relative to samples_over_distinct_papers by design, kept for readers summing the per-row syntology blocks"}}}