{"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/colnet-embedding-the-semantics-of-web-tables","title":"ColNet: Embedding the Semantics of Web Tables for Column Type Prediction","arxiv_id":"1811.01304","date":"2018-11-04","proceeding":null,"authors":["Jiaoyan Chen","Ernesto Jimenez-Ruiz","Ian Horrocks","Charles Sutton"],"abstract":"Automatically annotating column types with knowledge base (KB) concepts is a\ncritical task to gain a basic understanding of web tables. Current methods rely\non either table metadata like column name or entity correspondences of cells in\nthe KB, and may fail to deal with growing web tables with incomplete meta\ninformation. In this paper we propose a neural network based column type\nannotation framework named ColNet which is able to integrate KB reasoning and\nlookup with machine learning and can automatically train Convolutional Neural\nNetworks for prediction. The prediction model not only considers the contextual\nsemantics within a cell using word representation, but also embeds the\nsemantics of a column by learning locality features from multiple cells. The\nmethod is evaluated with DBPedia and two different web table datasets, T2Dv2\nfrom the general Web and Limaye from Wikipedia pages, and achieves higher\nperformance than the state-of-the-art approaches.","url_abs":"http://arxiv.org/abs/1811.01304v2","url_pdf":"http://arxiv.org/pdf/1811.01304v2.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":"colnet-embedding-the-semantics-of-web-tables","repo_url":"https://github.com/alan-turing-institute/SemAIDA","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"none","reach":{"status":"ok","spdx":"Apache-2.0"}}],"tasks":[{"task_slug":"column-type-annotation","task_name":"Column Type Annotation"},{"task_slug":"table-annotation","task_name":"Table annotation"},{"task_slug":"type-prediction","task_name":"Type prediction"},{"task_slug":"type","task_name":"Vocal Bursts Type Prediction"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/column-type-annotation-on-t2dv2","task":"Column Type Annotation","dataset":"T2Dv2","model":"ColNet - Ensemble","rank_in_archive_order":3,"of":3,"metrics":{"F1 (%)":"94.9"},"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}