{"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/dreifluss-a-minimalist-approach-for-table","title":"DREIFLUSS: A Minimalist Approach for Table Matching","arxiv_id":null,"date":"2023-11-20","proceeding":"SemTab@ISWC 2023 11","authors":["Vishvapalsinhji Parmar","Alsayed Algergawy"],"abstract":"This paper introduces DREIFLUSS, an innovative, minimalist approach designed to tackle the Column Type Annotation (CTA) and Column Property Annotation (CPA) tasks in the SemTab challenge. DREIFLUSS efficiently employs semantic information from well-established knowledge graphs, DBpedia, and Schema.org, to improve the annotation process. Experimental evidence illustrates the superior performance of logistic regression models trained via DREIFLUSS, resulting in precise column-type annotations and insightful relationship predictions. The findings substantiate the significance of proper sampling technique while training a model, thereby boosting the accuracy and efficiency of table matching. This research illuminates a promising pathway to enhance table matching techniques, underlining the practical ramifications of DREIFLUSS for data integration and knowledge discovery endeavors.","url_abs":"https://ceur-ws.org/Vol-3557/paper4.pdf","url_pdf":"https://ceur-ws.org/Vol-3557/paper4.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":"column-type-annotation","task_name":"Column Type Annotation"},{"task_slug":"columns-property-annotation","task_name":"Columns Property Annotation"},{"task_slug":"data-integration","task_name":"Data Integration"},{"task_slug":"knowledge-graphs","task_name":"Knowledge Graphs"}],"methods":[{"method_slug":"logistic-regression","method_name":"Logistic Regression"}],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/column-type-annotation-on-wdc-sotab-v2","task":"Column Type Annotation","dataset":"WDC SOTAB V2","model":"DREIFLUSS","rank_in_archive_order":3,"of":5,"metrics":{"Micro F1":"38.04"},"uses_additional_data":false},{"leaderboard":"/sota/columns-property-annotation-on-wdc-sotab-v2","task":"Columns Property Annotation","dataset":"WDC SOTAB V2","model":"DREIFLUSS","rank_in_archive_order":4,"of":4,"metrics":{"Micro F1":"17.39"},"uses_additional_data":false}],"syntology":{"syntology_url":null,"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}