{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/kgcode-tab-results-for-semtab-2022","title":"KGCODE-Tab Results for SemTab 2022","arxiv_id":null,"date":"2022-10-25","proceeding":"SemTab@ISWC 2022 10","authors":["Xinhe Li","Shuxin Wang","Wei Zhou","Gongrui Zhang","Chenghuan Jiang","Tianyu Hong and Peng Wang"],"abstract":"This paper presents the results of KGCODE-Tab in the tabular data to knowledge graph matching contest SemTab 2022. As an efficient tabular data linking system, KGCODE-Tab is intended to participate in three tasks of the content: Column Type Annotation (CTA), Cell Entity Annotation (CEA), and Columns Property Annotation (CPA). The specific techniques used by KGCODE-Tab will be introduced briefly. The strengths and weaknesses of KGCODE-Tab will also be discussed.","url_abs":"https://ceur-ws.org/Vol-3320/paper5.pdf","url_pdf":"https://ceur-ws.org/Vol-3320/paper5.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[],"tasks":[{"task_slug":"cell-entity-annotation","task_name":"Cell Entity Annotation"},{"task_slug":"column-type-annotation","task_name":"Column Type Annotation"},{"task_slug":"columns-property-annotation","task_name":"Columns Property Annotation"},{"task_slug":"graph-matching","task_name":"Graph Matching"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/cell-entity-annotation-on-biodivtab","task":"Cell Entity Annotation","dataset":"BiodivTab","model":"KGCODE-Tab","rank_in_archive_order":1,"of":5,"metrics":{"F1 (%)":"91.1"},"uses_additional_data":false},{"leaderboard":"/sota/cell-entity-annotation-on-toughtables-dbp","task":"Cell Entity Annotation","dataset":"ToughTables-DBP","model":"KGCODE-Tab","rank_in_archive_order":2,"of":5,"metrics":{"F1 (%)":"82.7"},"uses_additional_data":false},{"leaderboard":"/sota/column-type-annotation-on-biodivtab","task":"Column Type Annotation","dataset":"BiodivTab","model":"KGCODE-Tab","rank_in_archive_order":1,"of":6,"metrics":{"F1 (%)":"86.7"},"uses_additional_data":false},{"leaderboard":"/sota/column-type-annotation-on-gittables-semtab","task":"Column Type Annotation","dataset":"GitTables-SemTab-DBP","model":"KGCODE-Tab","rank_in_archive_order":1,"of":3,"metrics":{"F1 (%)":"58.7"},"uses_additional_data":false},{"leaderboard":"/sota/column-type-annotation-on-gittables-semtab-1","task":"Column Type Annotation","dataset":"GitTables-SemTab-SCH","model":"KGCODE-Tab","rank_in_archive_order":1,"of":2,"metrics":{"F1 (%)":"69.3"},"uses_additional_data":false},{"leaderboard":"/sota/column-type-annotation-on-toughtables-dbp","task":"Column Type Annotation","dataset":"ToughTables-DBP","model":"KGCODE-Tab","rank_in_archive_order":1,"of":6,"metrics":{"F1 (%)":"48"},"uses_additional_data":false},{"leaderboard":"/sota/column-type-annotation-on-toughtables-wd","task":"Column Type Annotation","dataset":"ToughTables-WD","model":"KGCODE-Tab","rank_in_archive_order":4,"of":5,"metrics":{"F1 (%)":"54.3"},"uses_additional_data":false}],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}