{"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/190600781","title":"Learning Semantic Annotations for Tabular Data","arxiv_id":"1906.00781","date":"2019-05-30","proceeding":null,"authors":["Jiaoyan Chen","Ernesto Jimenez-Ruiz","Ian Horrocks","Charles Sutton"],"abstract":"The usefulness of tabular data such as web tables critically depends on understanding their semantics. This study focuses on column type prediction for tables without any meta data. Unlike traditional lexical matching-based methods, we propose a deep prediction model that can fully exploit a table's contextual semantics, including table locality features learned by a Hybrid Neural Network (HNN), and inter-column semantics features learned by a knowledge base (KB) lookup and query answering algorithm.It exhibits good performance not only on individual table sets, but also when transferring from one table set to another.","url_abs":"https://arxiv.org/abs/1906.00781v1","url_pdf":"https://arxiv.org/pdf/1906.00781v1.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":"190600781","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":"prediction","task_name":"Prediction"},{"task_slug":"table-annotation","task_name":"Table annotation"},{"task_slug":"type-prediction","task_name":"Type prediction"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/column-type-annotation-on-t2dv2","task":"Column Type Annotation","dataset":"T2Dv2","model":"HNN + P2Vec","rank_in_archive_order":1,"of":3,"metrics":{"Accuracy (%)":"96.6"},"uses_additional_data":false},{"leaderboard":"/sota/column-type-annotation-on-wikipediags-cta","task":"Column Type Annotation","dataset":"WikipediaGS-CTA","model":"HNN","rank_in_archive_order":2,"of":2,"metrics":{"Accuracy (%)":"65.5"},"uses_additional_data":false}],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1906.00781","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}