{"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/magic-mining-an-augmented-graph-using-ink","title":"MAGIC: Mining an Augmented Graph using INK, starting from a CSV","arxiv_id":null,"date":"2021-10-01","proceeding":"SemTab@ISWC 2021 10","authors":["Bram Steenwinckel","Filip De Turck","Femke Ongenae"],"abstract":"A large portion of structured data does not yet reap the benefits of the Semantic Web. Therefore, The “Tabular Data to Knowledge Graph Matching” competition at ISWC tries to bridge this gap by evaluating and promoting the creation of such semantic annotations tools. Besides annotating data semantically, the system should also be able to further augment the datasets based on the provided annotations. In this paper, we propose a system that is capable of both annotating and augmenting a dataset by using the interpretable embedding technique INK. The “Tabular Data to Knowledge Graph Matching” competition was used to evaluate the proposed annotation capabilities of our proposed system.","url_abs":"https://www.semanticscholar.org/paper/MAGIC%3A-Mining-an-Augmented-Graph-using-INK%2C-from-a-Steenwinckel-Turck/03465d28e575ac8273887f0f56b67b890230d788","url_pdf":"http://ceur-ws.org/Vol-3103/paper6.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":[{"paper_slug":"magic-mining-an-augmented-graph-using-ink","repo_url":"https://github.com/IBCNServices/Magic","is_official":0,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[{"task_slug":"cell-entity-annotation","task_name":"Cell Entity Annotation"},{"task_slug":"column-type-annotation","task_name":"Column Type Annotation"},{"task_slug":"graph-matching","task_name":"Graph Matching"},{"task_slug":"table-annotation","task_name":"Table annotation"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/cell-entity-annotation-on-biodivtab","task":"Cell Entity Annotation","dataset":"BiodivTab","model":"MAGIC","rank_in_archive_order":5,"of":5,"metrics":{"F1 (%)":"10"},"uses_additional_data":false},{"leaderboard":"/sota/cell-entity-annotation-on-toughtables-dbp","task":"Cell Entity Annotation","dataset":"ToughTables-DBP","model":"MAGIC","rank_in_archive_order":5,"of":5,"metrics":{"F1 (%)":"18.4"},"uses_additional_data":false},{"leaderboard":"/sota/column-type-annotation-on-biodivtab","task":"Column Type Annotation","dataset":"BiodivTab","model":"MAGIC","rank_in_archive_order":5,"of":6,"metrics":{"F1 (%)":"14.2"},"uses_additional_data":false},{"leaderboard":"/sota/column-type-annotation-on-toughtables-dbp","task":"Column Type Annotation","dataset":"ToughTables-DBP","model":"MAGIC","rank_in_archive_order":6,"of":6,"metrics":{"F1 (%)":"15.9"},"uses_additional_data":false}],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}