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WIREs Data Mining Knowl Discov
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Table understanding approaches for extracting knowledge from heterogeneous tables

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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. This article is categorized under: Application Areas > Data Mining Software Tools Technologies > Data Preprocessing Technologies > Structure Discovery and Clustering
Examples of generic tables: (a) table with access keys positioned in the header, (b) table with access key mixed with its content, (c) table with access keys in the lateral stub
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Generation of a synthetic program through Foofah (Jin et al., 2017)
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Extraction phase in Senbazuru (Chen et al., 2013). (a) The system first provides an initial segmentation and functional analysis of the table for highlighting the table content and header/stub. Then, it proposes a possible hierarchy on the stub (b) that can be updated and repaired by the user (c). Starting from the identified hierarchies (on the header and on the stub) the relational tuple for each single value in the content area is generated (d). Finally, the relational tuples are collected into a relational table (e)
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Application of the transformation operations proposed by Kandel et al. (2011)
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Annotating table columns by exploiting the Katara system (Chu et al., 2015)
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Semantic annotation of a table by the approach proposed by Taheriyan et al. (2016)
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The system presented in the studies of Koci et al. (2016a, 2016b, 2017), Koci, Kuban, et al. (2019), and Koci, Thiele, et al. (2019)
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The incremental human‐in‐the‐loop procedure proposed by Chen, Dadiomov, et al. (2017)
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Approaches for location, segmentation, and functional and structural analysis (Costa e Silva, 2010; Embley, Krishnamoorthy, Nagy, & Seth, 2016; Gatterbauer, Bohunsky, Herzog, Krüpl, & Pollak, 2007; Nagy, Embley, Krishnamoorthy, & Seth, 2015)
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The conditional random field (CRF) document classification framework
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The table understanding approach
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A complex table with the results of a student satisfaction surveys on lesson organization. The table reports questions, answers by different groups (with the number of students per group), and the p‐value (p) resulting from a χ2 test of significance with degrees of freedom (df)
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General organization of a table
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Technologies > Structure Discovery and Clustering
Technologies > Data Preprocessing
Application Areas > Data Mining Software Tools

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