Automatic Classification of Scientific Papers in PDF for Populating Ontologies

Rendon-Miranda, Juan C.; Arana-Llanes, Julia Y.; Gonzalez-Serna, Juan G.; Gonzalez-Franco, Nimrod

doi:10.1109/csci.2014.153

Cited by 5 publications

(1 citation statement)

References 4 publications

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“…This paper [14] used the Naive Bayes algorithm to categorize scientific papers and use LA PDFText tools to extract text from PDF files. The scientific papers are described by defining different sections such as the author, keyword, title, etc.…”

Section: Related Work In Text Classificationmentioning

confidence: 99%

Analyzing Graduation Project Ideas by using Machine Learning

Alharbi

Alshaya

Alsheail

et al. 2021

Int. J. Interact. Mob. Technol.

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The graduation projects (GP) are important because it reflects the academic profile and achievement of the students. For many years’ graduation projects are done by the information technology department students. Most of these projects have great value, and some were published in scientific journals and international conferences. However, these projects are stored in an archive room haphazardly and there is a very small part of it is a set of electronic PDF files stored on hard disk, which wastes time and effort and cannot benefit from it. However, there is no system to classify and store these projects in a good way that can benefit from them. In this paper, we reviewed some of the best machine learning algorithms to classify text “graduation projects”, support vector machine (SVM) algorithm, logistic regression (LR) algorithm, random forest (RF) algorithm, which can deal with an extremely small amount of dataset after comparing these algorithms based on accuracy. We choose the SVM algorithm to classify the projects. Besides, we will mention how to deal with a super small dataset and solve this problem.

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Section: Related Work In Text Classificationmentioning

confidence: 99%

Analyzing Graduation Project Ideas by using Machine Learning

Alharbi

Alshaya

Alsheail

et al. 2021

Int. J. Interact. Mob. Technol.

View full text Add to dashboard Cite

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Hierarchical approach for scientific document classification

D'cunha¹,

Sen²

2015

International Conference on Computing, Communication &Amp; Automation

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Classification is the grouping of information or objects in predefined labeled categories based on similarities. Exponential growth rates of scientific document collection leads to unmanageable manual classification. Feature extraction is the central prerequisite of automatic document classification. TF-IDF (term frequency-inverse document frequency) is commonly used to express the text feature weight. This paper proposes a new feature weighting method by modifying TF-IDF formula.

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Construction of Dependency Structure Matrix (DSM) for e-books using ontology based text mining

Nanthini

Ramadoss

2015

International Confernce on Innovation Information in Computing Technologies

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Dependency Structure Matrix (DSM) is a square matrix, used to map the dependencies between each entity/element. Such dependencies are examined to make out impacts. In general, college libraries maintain e-books for various departments, as students, research scholars in these days have started to refer e-books in most of the times. Since we don't have hard copies for some of the books, we go for searching ebooks. This paper primarily focuses on finding related/linked/correlated/interrelated e-books that for a targeted e-book based on table of contents of each book. Related books will hold desired content that we require. Extract content page to ascertain the classes of e-books, ontology that provides inferences, regarding them. Subsequently, uses this ontology to discover the dependencies between each book and construct DSM which is used to represent related books as a result of comparing table of contents of each book. Subsequent to construction of DSM, it is converted into graph structure along with dependency level for easy analysis and find out. During arrival of fresh e-book to library, DSM is automatically updated by mining PDF and the graph is also updated along with their dependency level.

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Automatic Classification of Scientific Papers in PDF for Populating Ontologies

Cited by 5 publications

References 4 publications

Analyzing Graduation Project Ideas by using Machine Learning

Analyzing Graduation Project Ideas by using Machine Learning

Hierarchical approach for scientific document classification

Construction of Dependency Structure Matrix (DSM) for e-books using ontology based text mining

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