TabbyXL: Rule-Based Spreadsheet Data Extraction and Transformation

Shigarov, Alexey O.; Khristyuk, Vasiliy V.; Mikhailov, Andrey A.; Paramonov, Viacheslav

doi:10.1007/978-3-030-30275-7_6

Cited by 7 publications

(3 citation statements)

References 36 publications

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“…After the table localization, segmentation, and functional and structural analysis, several methods design algorithms for expressing the inferred structural relationships as relational models (JSON or XML formats) or RDF formats. TranSheet (Hung, Benatallah, & Saint‐Paul, 2011) and TabbyXL (Shigarov, Khristyuk, Mikhailov, & Paramonov, 2019) introduce languages for the specification of the transformations needed to simultaneously define the table functional and structural relationships and then convert them into a relational format. Moreover, Senbazuru (Chen et al, 2013) and HaExcel (Cunha et al, 2014) automatically extrapolate the table functional and structural relationships by applying some of the algorithms described in Section 3.2 and then exploit rule‐based algorithms to define and combine the relational tuples representing the structural relationships.…”

Section: Extracting and Transforming Tablesmentioning

confidence: 99%

“…Such labeling procedure allows defining hierarchical structural relationships, which are then transformed into a relational format. With TabbyXL (Shigarov et al, 2019), users may define functional and structural relationships by using either the standard DROOLS language (RedHat, 2020), or CRL (Shigarov & Mikhailov, 2017), a specifically developed rule-based language for implementing programs aimed at (a) cell cleansing issues (merging, splitting, updating data), (b) role analysis, to recover entities and labels as functional data items presented in tables (marking all the cells having the same role or located in the same functional region with a fixed value), (c) structural analysis (entity-label association), and (d) interpretation (labeling with a category).…”

Section: Basic Extraction and Transformation Toolsmentioning

confidence: 99%

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Table understanding approaches for extracting knowledge from heterogeneous tables

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Casiraghi

Mesiti

2021

WIREs Data Min & Knowl

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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

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Section: Extracting and Transforming Tablesmentioning

confidence: 99%

Section: Basic Extraction and Transformation Toolsmentioning

confidence: 99%

Table understanding approaches for extracting knowledge from heterogeneous tables

Bonfitto

Casiraghi

Mesiti

2021

WIREs Data Min & Knowl

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“…[5]), data transformation (e.g. [6]), programming by example (e.g. [4]), and semantic characterization of the information (e.g.…”

Section: Introductionmentioning

confidence: 99%

Semantic Integration of Heterogeneous and Complex Spreadsheet Tables

Bonfitto

2021

Database Systems for Advanced Applications

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A great number of companies and institutions use spreadsheets for managing, publishing and sharing their data. Though effective, spreadsheets are mainly designed for being interpreted by humans, and the automatic extraction of their content and interpretation is a complex task. The task becomes even harder when tables present different kinds of mistakes and their layout is complex. In this paper, we outline the approach that we wish to develop during the PhD for answering the research question "how to semi-automatically extract coherent semantic information from heterogeneous and complex spreadsheets?".

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