Integrated knowledge content in an interdisciplinary field: identification, classification, and application

Wang, Shiyun; Mao, Jin; Cao, Yongqiang; Li, Gang

doi:10.1007/s11192-022-04282-0

Cited by 7 publications

(3 citation statements)

References 69 publications

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“…Based on text mining or entity extraction, the knowledge units are extracted to identify the discipline to which the knowledge units belong. Knowledge unit distribution (Xu et al, 2016;Wang et al, 2022), citation locations (Wang et al, 2021a, b) and coword network (Ba et al, 2019;Engerer, 2017) are used to characterize the diffusion, transfers and integration of knowledge among different disciplines and thus to reveal the interdisciplinarity of the research. Currently, knowledge units are mainly represented with terms, knowledge memes, keywords and topics contained in the publications (Wang et al, 2021a, b;Ba et al, 2019;Engerer, 2017;Li et al, 2021;Cheng et al, 2020;Mao et al, 2020), focusing on revealing the knowledge content in the research and expressing the details of knowledge interaction between different disciplines from a micro perspective.…”

Section: Interdisciplinarity Researchmentioning

confidence: 99%

Interdisciplinarity of information science: an evolutionary perspective of theory application

Zhang,

Wang,

Huang

et al. 2023

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Purpose This paper aims to reveal the interdisciplinarity of information science (IS) from the perspective of the evolution of theory application.Design/methodology/approach Select eight representative IS journals as data sources, extract the theories mentioned in the full texts of the research papers and then measure annual interdisciplinarity of IS by conducting theory co-occurrence network analysis, diversity measure and evolution analysis.Findings As a young and vibrant discipline, IS has been continuously absorbing and internalizing external theoretical knowledge and thus formed a high degree of interdisciplinarity. With the continuous application of some kernel theories, the interdisciplinarity of IS appears to be decreasing and gradually converging into a few neighboring disciplines. Influenced by big data and artificial intelligence, the research paradigm of IS is shifting from a theory centered one to a technology centered one.Research limitations/implications This study helps to understand the evolution of the interdisciplinarity of IS in the past 21 years. The main limitation is that the data were collected from eight journals indexed by the Social Sciences Citation Index and a small amount of theories might have been omitted.Originality/value This study identifies the kernel theories in IS research, measures the interdisciplinarity of IS based on the evolution of the co-occurrence network of theory source disciplines and reveals the paradigm shift being happening in IS.

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Section: Interdisciplinarity Researchmentioning

confidence: 99%

Interdisciplinarity of information science: an evolutionary perspective of theory application

Zhang,

Wang,

Huang

et al. 2023

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“…Leveraging machine learning and bibliometrics in literature reviews ature (S. Wang et al 2022). The tradeoff is ceding some control to the algorithms.…”

Section: Analyzing and Summarizing Publication Contentmentioning

confidence: 99%

Leveraging machine learning and bibliometrics in literature reviews

Karlstrøm

2023

Preprint

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With a growing researcher population, increased researcher interconnectivity, disciplinary fragmentation and lowered cost of manuscript processing and distribution, the speed and volume of scientific publishing is accelerating, both in terms of number of authors and in terms of the number of publications by each author. For systematic reviewers, this poses a challenge in the balance between not excluding too many possibly relevant studies ex ante and handling increasingly large corpora that result from document retrieval. Efforts to summarize and synthesize knowledge within or across domains are increasingly reliant on computational methods for document retrieval and analysis, but questions remain about the coverage and quality of automated review procedures. This article presents a workflow-centered framework for computational techniques for automated content analysis in the traditional workflow of systematic or scoping reviews. It provides guidelines for handling large document sets founded in best practice rather than relying on arbitrary search constraints to limit the number of publications under consideration and provides a discussion of where current efforts are headed and possible future developments using computational techniques, with a particular focus on the advent of large language models in ingesting and summarizing large knowledge corpora.

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“…To demystify the characteristics of knowledge integration in interdisciplinary research from a perspective of knowledge content, Wang et al (2022) examined the best performance of the BERT model in an eHealth dataset when representing integrated knowledge phrases shared between citation sentences and cited references and classifying them into given knowledge categories.…”

Section: Ai + Informetrics For Interdisciplinary Measuresmentioning

confidence: 99%

An editorial of “AI + informetrics”: multi-disciplinary interactions in the era of big data

Zhang

Mayr

et al. 2022

Scientometrics

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Integrated knowledge content in an interdisciplinary field: identification, classification, and application

Cited by 7 publications

References 69 publications

Interdisciplinarity of information science: an evolutionary perspective of theory application

Interdisciplinarity of information science: an evolutionary perspective of theory application

Leveraging machine learning and bibliometrics in literature reviews

An editorial of “AI + informetrics”: multi-disciplinary interactions in the era of big data

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