Building the Dresden Web Table Corpus: A Classification Approach

Eberius, Julian; Braunschweig, Katrin; Hentsch, Markus; Thiele, Maik; Ahmadov, Ahmad; Lehner, Wolfgang

doi:10.1109/bdc.2015.30

Cited by 64 publications

(62 citation statements)

References 15 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%

“…Most authors differentiate between data tables, which provide data to be extracted, and non-data tables, which are used for layout purposes or to provide utilities. Many of them make also a difference between listings, forms, matrices, and enumerations [16,18,26,30,34,44,46,66], although the exact terminology used is very diverging; there is also a proposal in which tables are classified according to whether they have headers or not [22].…”

Section: Table-related Vocabularymentioning

confidence: 99%

“…In this context, data extraction consists in transforming tables into structured formats that focus on their data and abstract away from how they are displayed. Data extraction has many applications to text mining [24,64,65], data (meta-)search [3,9,18,26,44,51,[63][64][65], query expansion [16], document summarisation [40,64], question answering [1,20,44,46,65], knowledge discovery [9,22,26,32,44,46], knowledge base construction [17,72], knowledge augmentation [1,9,18,20,56,56,57,67], synonym finding [1,3,39], improving accessibility [43,47,49,64,65], textual advertising [15], data compression [2,49], or creating linked data…”

Section: Introductionmentioning

confidence: 99%

See 2 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: Table-related Vocabularymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

On extracting data from tables that are encoded using HTML

Roldán

Jiménez

Corchuelo

2020

Knowledge-Based Systems

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“…Chu et al [13] further introduced syntactic and semantic coherence measures for list extraction, and learned semantic coherence measures by leveraging column co-occurence statistics from existing web tables. Several researchers produced web tables from the public Common Crawl [1, 24,15], thereby making them available to a broad audience outside the large Web companies. Wang, et al [36] improved extraction quality by leveraging curated knowledge bases.…”

Section: Extractionmentioning

confidence: 99%

Ten years of webtables

Cafarella

Halevy²,

Lee

et al. 2018

Proc. VLDB Endow.

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In 2008, we wrote about WebTables, an effort to exploit the large and diverse set of structured databases casually published online in the form of HTML tables. The past decade has seen a flurry of research and commercial activities around the WebTables project itself, as well as the broad topic of informal online structured data. In this paper, we 1 will review the WebTables project, and try to place it in the broader context of the decade of work that followed. We will also show how the progress over the past ten years sets up an exciting agenda for the future, and will draw upon many corners of the data management community.

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“…It also discusses heuristics, which are based on features similar to the paper above, to classify Web tables into the proposed taxonomy. In (Eberius et al, 2015), the authors describe the creation of the Dresden Web Table Corpus, by proposing a classification approach that works on the level of different table layout classes.…”

Section: Table Recognition and Layout Discoverymentioning

confidence: 99%

A Machine Learning Approach for Layout Inference in Spreadsheets

Koci

Thiele

Romero

et al. 2016

Proceedings of the 8th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management

Self Cite

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Abstract:Spreadsheet applications are one of the most used tools for content generation and presentation in industry and the Web. In spite of this success, there does not exist a comprehensive approach to automatically extract and reuse the richness of data maintained in this format. The biggest obstacle is the lack of awareness about the structure of the data in spreadsheets, which otherwise could provide the means to automatically understand and extract knowledge from these files. In this paper, we propose a classification approach to discover the layout of tables in spreadsheets. Therefore, we focus on the cell level, considering a wide range of features not covered before by related work. We evaluated the performance of our classifiers on a large dataset covering three different corpora from various domains. Finally, our work includes a novel technique for detecting and repairing incorrectly classified cells in a post-processing step. The experimental results show that our approach delivers very high accuracy bringing us a crucial step closer towards automatic table extraction.

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Building the Dresden Web Table Corpus: A Classification Approach

Cited by 64 publications

References 15 publications

On extracting data from tables that are encoded using HTML

On extracting data from tables that are encoded using HTML

Ten years of webtables

A Machine Learning Approach for Layout Inference in Spreadsheets

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