从异构表中提取知识的表理解方法

IF 6.4 2区 计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Wiley Interdisciplinary Reviews-Data Mining and Knowledge Discovery Pub Date : 2021-03-28 DOI:10.1002/widm.1407
Sara Bonfitto, E. Casiraghi, M. Mesiti
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引用次数: 16

摘要

表理解方法提取、转换和解释嵌入在不同格式的文档/文件中的表格数据中包含的信息。这种自动理解将允许利用表格信息,以准确地回答查询,或在公共知识库中集成异构信息库,或在不同来源之间交换信息。本调查的目的是对迄今为止致力于表理解问题的研究工作进行全面分析,并描述支持将异构表转换为有意义信息的系统。
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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.
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来源期刊
Wiley Interdisciplinary Reviews-Data Mining and Knowledge Discovery
Wiley Interdisciplinary Reviews-Data Mining and Knowledge Discovery COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE-COMPUTER SCIENCE, THEORY & METHODS
CiteScore
22.70
自引率
2.60%
发文量
39
审稿时长
>12 weeks
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.
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