{"title":"从异构表中提取知识的表理解方法","authors":"Sara Bonfitto, E. Casiraghi, M. Mesiti","doi":"10.1002/widm.1407","DOIUrl":null,"url":null,"abstract":"Table understanding methods extract, transform, and interpret the information contained in tabular data embedded in documents/files of different formats. Such automatic understanding would allow to exploit tabular information with the aim of accurately answering queries, or integrating heterogeneous repositories of information in a common knowledge base, or exchanging information among different sources. The purpose of this survey is to provide a comprehensive analysis of the research efforts so far devoted to the problem of table understanding and to describe systems that support the transformation of heterogeneous tables into meaningful information.","PeriodicalId":48970,"journal":{"name":"Wiley Interdisciplinary Reviews-Data Mining and Knowledge Discovery","volume":"686 1","pages":""},"PeriodicalIF":6.4000,"publicationDate":"2021-03-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"16","resultStr":"{\"title\":\"Table understanding approaches for extracting knowledge from heterogeneous tables\",\"authors\":\"Sara Bonfitto, E. Casiraghi, M. Mesiti\",\"doi\":\"10.1002/widm.1407\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Table understanding methods extract, transform, and interpret the information contained in tabular data embedded in documents/files of different formats. Such automatic understanding would allow to exploit tabular information with the aim of accurately answering queries, or integrating heterogeneous repositories of information in a common knowledge base, or exchanging information among different sources. The purpose of this survey is to provide a comprehensive analysis of the research efforts so far devoted to the problem of table understanding and to describe systems that support the transformation of heterogeneous tables into meaningful information.\",\"PeriodicalId\":48970,\"journal\":{\"name\":\"Wiley Interdisciplinary Reviews-Data Mining and Knowledge Discovery\",\"volume\":\"686 1\",\"pages\":\"\"},\"PeriodicalIF\":6.4000,\"publicationDate\":\"2021-03-28\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"16\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Wiley Interdisciplinary Reviews-Data Mining and Knowledge Discovery\",\"FirstCategoryId\":\"94\",\"ListUrlMain\":\"https://doi.org/10.1002/widm.1407\",\"RegionNum\":2,\"RegionCategory\":\"计算机科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Wiley Interdisciplinary Reviews-Data Mining and Knowledge Discovery","FirstCategoryId":"94","ListUrlMain":"https://doi.org/10.1002/widm.1407","RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE","Score":null,"Total":0}
Table understanding approaches for extracting knowledge from heterogeneous tables
Table understanding methods extract, transform, and interpret the information contained in tabular data embedded in documents/files of different formats. Such automatic understanding would allow to exploit tabular information with the aim of accurately answering queries, or integrating heterogeneous repositories of information in a common knowledge base, or exchanging information among different sources. The purpose of this survey is to provide a comprehensive analysis of the research efforts so far devoted to the problem of table understanding and to describe systems that support the transformation of heterogeneous tables into meaningful information.
期刊介绍:
The goals of Wiley Interdisciplinary Reviews-Data Mining and Knowledge Discovery (WIREs DMKD) are multifaceted. Firstly, the journal aims to provide a comprehensive overview of the current state of data mining and knowledge discovery by featuring ongoing reviews authored by leading researchers. Secondly, it seeks to highlight the interdisciplinary nature of the field by presenting articles from diverse perspectives, covering various application areas such as technology, business, healthcare, education, government, society, and culture. Thirdly, WIREs DMKD endeavors to keep pace with the rapid advancements in data mining and knowledge discovery through regular content updates. Lastly, the journal strives to promote active engagement in the field by presenting its accomplishments and challenges in an accessible manner to a broad audience. The content of WIREs DMKD is intended to benefit upper-level undergraduate and postgraduate students, teaching and research professors in academic programs, as well as scientists and research managers in industry.