Disentangling the Structure of Tables in Scientific Literature

Milošević, Nikola; Gregson, Cassie; Hernandez, Robert; Nenadić, Goran

doi:10.1007/978-3-319-41754-7_14

Cited by 22 publications

(58 citation statements)

References 24 publications

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“…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%

“…Lerman et al [ [22], and Milošević et al [44] did not pay attention to the discrimination task. The other proposals provide sophisticated approaches.…”

Section: Discriminationmentioning

confidence: 99%

“…Milošević et al [44] restricted their attention to tables from the PubMed Central repository. Their focus is on identifying the meta-data cells, since the other cells are considered data cells by default.…”

Section: Functional Analysismentioning

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 3 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%

“…Lerman et al [ [22], and Milošević et al [44] did not pay attention to the discrimination task. The other proposals provide sophisticated approaches.…”

Section: Discriminationmentioning

confidence: 99%

Section: Functional Analysismentioning

confidence: 99%

Section: Introductionmentioning

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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“…Figure retrieval has been considered, mainly focusing on using text from figure captions (Hearst et al, 2007), text within figures (Rodriguez-Esteban and Iossifov, 2009), as well as text from paragraphs discussing the figures and NER (Demner-Fushman et al, 2012). Research on information extraction from tables is rare (Wong et al, 2009;Peng et al, 2015;Milosevic et al, 2016), though this may change with recent availability of corpora (Shmanina et al, 2016). Jimeno-Yepes and Verspoor (2014) showed that most literature-curated mutation and genetic variant existed only as supplementary material and used open-source PDF conversion tools to extract text from supplementary files for text mining.…”

Section: Challenges and Directionsmentioning

confidence: 99%

Biomedical Text Mining for Research Rigor and Integrity: Tasks, Challenges, Directions

Kilicoglu

2017

Preprint

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An estimated quarter of a trillion US dollars is invested in the biomedical research enterprise annually. There is growing alarm that a significant portion of this investment is wasted, due to problems in reproducibility of research findings and in the rigor and integrity of research conduct and reporting. Recent years have seen a flurry of activities focusing on standardization and guideline development to enhance the reproducibility and rigor of biomedical research. Research activity is primarily communicated via textual artifacts, ranging from grant applications to journal publications. These artifacts can be both the source and the end result of practices leading to research waste. For example, an article may describe a poorly designed experiment, or the authors may reach conclusions not supported by the evidence presented. In this article, we pose the question of whether biomedical text mining techniques can assist the stakeholders in the biomedical research enterprise in doing their part towards enhancing research integrity and rigor. In particular, we identify four key areas in which text mining techniques can make a significant contribution: plagiarism/fraud detection, ensuring adherence to reporting guidelines, managing information overload, and accurate citation/enhanced bibliometrics. We review the existing methods and tools for specific tasks, if they exist, or discuss relevant research that can provide guidance for future work. With the exponential increase in biomedical research output and the ability of text mining approaches to perform automatic tasks at large scale, we propose that such approaches can add checks and balances that promote responsible research practices and can provide significant benefits for the biomedical research enterprise.

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Table understanding approaches for extracting knowledge from heterogeneous tables

Bonfitto

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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Disentangling the Structure of Tables in Scientific Literature

Cited by 22 publications

References 24 publications

On extracting data from tables that are encoded using HTML

On extracting data from tables that are encoded using HTML

Biomedical Text Mining for Research Rigor and Integrity: Tasks, Challenges, Directions

Table understanding approaches for extracting knowledge from heterogeneous tables

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