Research on Web Table Positioning Technology Based on Table Structure and Heuristic Rules

Tao, Ling; Liu, Tianqi; Zhang, Shunxiang; Liu, Zongtian

doi:10.1007/978-3-319-67071-3_41

Cited by 2 publications

(12 citation statements)

References 6 publications

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“…Unfortunately, there is not a consensus definition in the literature regarding what a table is. Many authors focus on the encoding since they define them as whatever one can encode within HTML table tags [3,7,9,16,18,28,30,32,34,38,47,49,49,59,66,68], which is a pragmatic approach; a few also refer to the display of data, since they define tables as grids in which data are located in cells in a manner that lines and/or styles ease interpreting them [22,24,26,28,30,32,46,49]. There is only a proposal that deviates a little from the previous approaches [21] since the authors focus on the data model behind the tables, independently from how they are displayed; their proposal, however, works on tables in which data are arranged in grids.…”

Section: Table-related Vocabularymentioning

confidence: 99%

“…Note that our summaries are intended to provide an overall picture using the vocabulary that we have presented previously; please, refer to the original articles for the full descriptions. [66], and Liao et al [38] reported on naive approaches that consisted in extracting every HTML excerpt with a table tag; Penn et al [49], Wang and Hu [64], and Crestan and Pantel [16] followed the same approach but discarded tables with nested tables. The other proposals provide more sophisticated approaches.…”

Section: Summary Of Proposalsmentioning

confidence: 99%

“…Penn et al [ [46], and Liao et al [38] did not report on any proposals to implement this task. Chen et al [7], Yang and Luk [68], Jung and Kwon [30] relied on a naive approach that searches for the cells using specific tags.…”

Section: Segmentationmentioning

confidence: 99%

“…Lerman et al [ [46], and Liao et al [38] did not report on any proposals to implement the functional analysis task. Gatterbauer et al [26] presented a naive approach that matches the structure of a table to a number of pre-defined structures in which it is also relatively easy to find the meta-data cells.…”

Section: Functional Analysismentioning

confidence: 99%

“…Penn et al [49], Hurst [28], Wang and Hu [64], Kim [46], and Liao et al [38] did not report on any proposals to implement the structural analysis task. Chen et al [7] presented a naive proposal that works on tables that provide data about a single entity, so all of the data cells form a single tuple; regarding the meta-data cells, they group them into headers horizontally or vertically after expanding spanned cells.…”

Section: Structural Analysismentioning

confidence: 99%

See 4 more Smart Citations

On extracting data from tables that are encoded using HTML

Roldán

Jiménez

Corchuelo

2020

Knowledge-Based Systems

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Tables are a common means to display data in human-friendly formats. Many authors have worked on proposals to extract those data back since this has many interesting applications. In this article, we summarise and compare many of the proposals to extract data from tables that are encoded using HTML and have been published between 2000 and 2018. We first present a vocabulary that homogenises the terminology used in this field; next, we use it to summarise the proposals; finally, we compare them side by side. Our analysis highlights several challenges to which no proposal provides a conclusive solution and a few more that have not been addressed sufficiently; simply put, no proposal provides a complete solution to the problem, which seems to suggest that this research field shall keep active in the near future. We have also realised that there is no consensus regarding the datasets and the methods used to evaluate the proposals, which hampers comparing the experimental results.

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Section: Table-related Vocabularymentioning

confidence: 99%

Section: Summary Of Proposalsmentioning

confidence: 99%

Section: Segmentationmentioning

confidence: 99%

Section: Functional Analysismentioning

confidence: 99%

Section: Structural Analysismentioning

confidence: 99%

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On extracting data from tables that are encoded using HTML

Roldán

Jiménez

Corchuelo

2020

Knowledge-Based Systems

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Classification of Layout vs. Relational Tables on the Web: Machine Learning with Rendered Pages

Haider

Yeşi̇lada

2022

ACM Trans. Web

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Table mining on the web is an open problem, and none of the previously proposed techniques provides a complete solution. Most research focuses on the structure of the HTML document, but because of the nature and structure of the web, it is still a challenging problem to detect relational tables. Web Content Accessibility Guidelines (WCAG) also cover a wide range of recommendations for making tables accessible, but our previous work shows that these recommendations are also not followed; therefore tables are still inaccessible to disabled people and automated processing. We propose a new approach to table mining by not looking at the HTML structure, but rather, the rendered pages by the browser. The first task in table mining on the web is to classify relational vs layout tables, and here we propose two alternative approaches for that task. We first introduce our dataset, which includes 725 web pages with 9957 extracted tables. Our first approach extracts features from a page after being rendered by the browser then applies several machine learning algorithms in classifying the layout vs relational tables. The best result is with Random Forest with the accuracy of 97.2% (F1-score: 0.955) with 10-fold cross-validation. Our second approach classifies tables using images taken from the same sources using Convolutional Neural Network (CNN) which gives an accuracy of 95% (F1-score: 0.95). Our work here shows that the web’s true essence comes after it goes through a browser and using the rendered pages and tables, the classification is more accurate compared to literature and paves the way in making the tables more accessible.

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Research on Web Table Positioning Technology Based on Table Structure and Heuristic Rules

Cited by 2 publications

References 6 publications

On extracting data from tables that are encoded using HTML

On extracting data from tables that are encoded using HTML

Classification of Layout vs. Relational Tables on the Web: Machine Learning with Rendered Pages

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