Keyphrase Extraction in Scholarly Digital Library Search Engines

Patel, Krutarth; Caragea, Cornelia; Wu, Jian; Giles, C. Lee

doi:10.1007/978-3-030-59618-7_12

Cited by 6 publications

(6 citation statements)

References 41 publications

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“…Extracting the domain of papers [10,13,24,25,[28][29][30][31] Extracting the domain of articles and creating profile of articles for recommendation Big data platform [30,31] They are implemented using big data system platforms, which are suitable for systems with huge data c. How has the relevance of textual information in recommender systems for the academic domain previously used by researchers been discovered and understood.…”

Section: Used By Descriptionmentioning

confidence: 99%

Paper Recommender System Using Big Data Tools

Jokar¹,

Esfandiari²,

Aghamirzadeh³

et al. 2024

Optimization Algorithms - Classics and Recent Advances

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To face the problem of information overload, digital libraries, like other businesses, have used recommender systems and try to personalize recommendations to users by using the textual information of papers. This textual information includes title, abstract, keywords, publisher, author and other similar items. Since the volume of papers is increasing day by day and recommender systems do not have the ability to cover this huge volume to process papers according to the user’s tastes, that is why we need to use our papers to cover and process this volume quickly. We have big data tools, which will offer relevant recommendations by running parallel processing. In this chapter, the researches and researches of researchers in the field of recommender systems/aware of the text of scientific papers and recommender systems have been discussed.

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Section: Used By Descriptionmentioning

confidence: 99%

Paper Recommender System Using Big Data Tools

Jokar¹,

Esfandiari²,

Aghamirzadeh³

et al. 2024

Optimization Algorithms - Classics and Recent Advances

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show abstract

“…These rapidly growing scholarly document collections offer rich domains of specific information for knowledge discovery, but they also pose many challenges to the navigation of and search for useful information. 17 Paula Younger and Kate Boddy describe how the vast amount of databases associated with a single interface can deliver duplicate studies.…”

Section: Databasesmentioning

confidence: 99%

Integrating Information Literacy with the Evidence-based Framework and Military Judgement and Decision Making: A Literature Review

McCullin¹

2023

ExpwMCUP

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This article offers a literature review that results in the introduction of three information literacy protocols. It is the third in a four-part series of articles that discusses the integration of the evidence-based framework (EBF) and military judgment and decision making (MJDM). The series is written as a conceptualization and implementation of the U.S. presidential memorandum on restoring faith in government dated 27 January 2021. The memorandum directs federal agencies to base policy- and decision-making processes on the best available evidence. Accordingly, the focus of the series is on integrating the EBF into defense planning and decision making as an operational art. This article focuses on information literacy—specifically, building a data set for systematic reviews. The current literature on information literacy reveals that specific protocols to enhance rigor, transparency, validity, and reliability in building a data set for systematic reviews are significantly underrepresented. In this article, the Stanford Design Thinking Process model was applied to address this underrepresentation. The Stanford model uses five phases of collaboration to develop new protocols. The results of these collaboration are three new protocols for building data sets that are specifically designed for an integration of the EBF and MJDM: the targeting search strategy protocol (TSSP); the research question variable synthesis protocol (RQVSP); and the human source query protocol (HSQP).

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“…The possible metadata fields [26, 14.3.3] are as follows: Despite the existence of a PDF metadata standard, existing research [6], [21], [27] continually indicates reading data from PDF metadata fields is a serious challenge. To that end, much of the current literature attempts to establish metadata extraction mechanisms [6], [7], [20], [23]. At the same time, development of automated tools, techniques, and practices presupposes keyword metadata is populated with author supplied content.…”

Section: Metadata Standardsmentioning

confidence: 99%

“…At the same time, development of automated tools, techniques, and practices presupposes keyword metadata is populated with author supplied content. Based on existing research [7], a majority (62%) of papers do not have keyword metadata available. The extent to which cybersecurity education literature follows the same trend is an open question.…”

Section: Metadata Standardsmentioning

confidence: 99%

“…Yet, the literature [6] suggests keywords in PDF metadata are underutilized by common search mechanisms. As a baseline, one study [7] found 62% of papers do not include author supplied keywords. As such, the purpose of this work was to examine a sample of cybersecurity laboratory education research and utilize statistical analyses to describe structural features related to discoverability (i.e., metadata-based keywords, keyword frequencies, and citations counts).…”

Section: Introductionmentioning

confidence: 99%

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Structure or Anarchy

Pittman

Barker

Alaee

2023

CISSE

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Bibliometric analysis is essential for understanding the growth, health, and trajectory of scientific disciplines. In effect, such analyses help researchers determine if a given field is well-structured or fragmented through anarchy. Prior work examined to what extent cybersecurity education research generated a follow-up study. The goal of the work was to uncover bibliometric features and characteristics linked to overall maturity of the field. The results suggested little, if any, research follow up or extension took place based on the dearth of interlinking between citations. This work continues the line of bibliometric description by investigating if cybersecurity education papers are not extended because of discoverability issues during literature reviews. To answer this question, this work explored structural bibliometric indicators in 163 journal and conference articles. Specifically, we extracted metadata keywords and paper content keywords as input to frequency analyses of the sample articles. The results revealed 12.4% of the sample contains metadata keywords. Further, 18.03% of the sample contained educated related keywords. Lastly, four of the top five sample papers by citation count do not contain keywords at all and papers with content only keywords exhibited more frequent citation than those with only metadata keywords. Based on these results, we offer observational conclusions as well as notions for future work.

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Keyphrase Extraction in Scholarly Digital Library Search Engines

Cited by 6 publications

References 41 publications

Paper Recommender System Using Big Data Tools

Paper Recommender System Using Big Data Tools

Integrating Information Literacy with the Evidence-based Framework and Military Judgement and Decision Making: A Literature Review

Structure or Anarchy

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