Citation Function Classification Based on Ontologies and Convolutional Neural Networks

Bakhti, Khadidja; Niu, Zhendong; Yousif, Abdallah; Nyamawe, Ally S.

doi:10.1007/978-3-319-95522-3_10

Cited by 8 publications

(2 citation statements)

References 19 publications

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“…In the field of bibliometrics, analyzing and classifying citations has become an emerging research topic in recent years in order to understand authors' motivations for citing literature (Bakhti, Niu, Yousif, & Nyamawe, 2018) and to gain a better understanding of the relationship between citing and cited works (Bornmann & Daniel, 2008). In terms of methodology, two approaches have been employed to determine the reasons for citing or the functions of citations (Bornmann & Daniel, 2008):…”

Section: Empirical Results Of Studies On Citationsmentioning

confidence: 99%

What do citation counts measure? An updated review of studies on citations in scientific documents published between 2006 and 2018

2019

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The purpose of this paper is to update the review of Bornmann and Daniel (2008) presenting a narrative review of studies on citations in scientific documents. The current review covers 41 studies published between 2006 and 2018. Bornmann and Daniel (2008) focused on earlier years. The current review describes the (new) studies on citation content and context analyses as well as the studies that explore the citation motivation of scholars through surveys or interviews. One focus in this paper is on the technical developments in the last decade, such as the richer meta-data available and machine-readable formats of scientific papers. These developments have resulted in citation context analyses of large datasets in comprehensive studies (which was not possible previously). Many studies in recent years have used computational and machine learning techniques to determine citation functions and polarities, some of which have attempted to overcome the methodological weaknesses of previous studies. The automated recognition of citation functions seems to have the potential to greatly enhance citation indices and information retrieval capabilities. Our review of the empirical studies demonstrates that a paper may be cited for very different scientific and non-scientific reasons. This result accords with the finding by Bornmann and Daniel (2008). The current review also shows that to better understand the relationship between citing and cited documents, a variety of features should be analyzed, primarily the citation context, the semantics and linguistic patterns in citations, citation locations within the citing document, and citation polarity (negative, neutral, positive).

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Section: Empirical Results Of Studies On Citationsmentioning

confidence: 99%

What do citation counts measure? An updated review of studies on citations in scientific documents published between 2006 and 2018

2019

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“…Kulkarni [39] proposed an automated citation extraction and analysis system that utilizes machine learning algorithms to identify and classify citation contexts based on their functions and intents. Bakhti et al [40] proposed a classification model that combines ontology and convolutional neural network (CNN) for categorizing citations into six categories. Researchers have also delved into the use of citation patterns and co-citation analysis to uncover connections and relationships among scholarly works.…”

Section: Related Workmentioning

confidence: 99%

Exploiting Contextual Word Embedding for Identification of Important Citations: Incorporating Section-Wise Citation Counts and Metadata Features

Iqbal,

Shahid,

Roman

et al. 2023

IEEE Access

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Finding relevant research papers is a challenging task due to the enormous number of scientific publications published each year. In recent years, the scientific community has delved into the analysis of citations at a deep level, specifically examining the content of the papers, in order to identify more important documents. Citations serve as potential parameters for determining linkages between research articles. They have been extensively used for various academic purposes, such as calculating journal impact factors, determining researchers' h-index, allocating research grants, and identifying the latest research trends. However, it has been argued by researchers that not all citations are equally influential. As a result, alternative techniques have been proposed to identify important citations based on content, metadata, and bibliographic information. Nevertheless, the current state-of-the-art approaches still require further improvement. Additionally, the use of deep learning models and word embedding techniques in this context has not been extensively studied. In this research work, we propose an approach based on two primary modules: 1) Section-wise citation count, and 2) metadata-based analysis of citation intent. Our study also involves conducting several experiments using deep learning models combined with FastText, word2vec, and BERT-based word embeddings to perform citation analysis. These experiments were conducted on two benchmark datasets, and the results were compared with a contemporary study that employed a rich set of content-based features for classification. Our findings indicate that the deep learning CNN model coupled with FastText word embedding achieves the best results in terms of accuracy, precision, and recall. It outperforms the existing state-of-the-art model with a precision score of 0. 97.

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A Semantic Representation of the Citation Structure

Skulimowski

2019

Metadata and Semantic Research

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Citation Function Classification Based on Ontologies and Convolutional Neural Networks

Cited by 8 publications

References 19 publications

What do citation counts measure? An updated review of studies on citations in scientific documents published between 2006 and 2018

What do citation counts measure? An updated review of studies on citations in scientific documents published between 2006 and 2018

Exploiting Contextual Word Embedding for Identification of Important Citations: Incorporating Section-Wise Citation Counts and Metadata Features

A Semantic Representation of the Citation Structure

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