Quantifying cross-disciplinary knowledge flow from the perspective of content: Introducing an approach based on knowledge memes

Mao, Jin; Liang, Zhentao; Cao, Yongqiang; Li, Gang

doi:10.1016/j.joi.2020.101092

Cited by 22 publications

(12 citation statements)

References 54 publications

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“…One approach is to rely on networks of citations and/or authorships, to infer category membership, similar to some of the proposed measures. Another is to link knowledge to disciplines not via papers but via knowledge memes, as has been proposed in the area of knowledge diffusion (Mao et al, 2020) or other techniques that leverage text mining Karlovčec & Mladenić, 2015;Craven et al, 2019). A third approach is to infer disciplines by examining the references of the cited and identify the most dominant discipline in this set (Glänzel et al, 2021).…”

Section: Discussionmentioning

confidence: 99%

Toward greater consistency and validity in measuring interdisciplinarity: a systematic and conceptual evaluation

2022

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While interdisciplinary research (IDR) has attracted much attention, this has not yet resulted in a coherent body of knowledge of interdisciplinarity. One of the impediments is a lack of consensus on its conceptualization and measurement. Some of the proposed measures have shown to misalign empirically, meaning that conclusions about IDR can differ across measures. To clarify this disagreement conceptually, and to stimulate better coherence in measurement, this paper starts with a review of the IDR definitions. From a synthesis of these definitions, we provide a conceptual definition and a logical structure of the construct, and derive evaluation criteria for its measures. We use these to evaluate 21 measures of IDR. The results show that measures vary widely in meeting the criteria, which can explain some of the observed inconsistencies in earlier studies. We discuss the most common limitations and present empirical analyses to gauge their severity. We present several suggestions for future measurement of the interdisciplinarity of research. We hope that with these suggestions, researchers can draw more consistent conclusions, aiding in the development of a coherent body of knowledge of this ever-important phenomenon.

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Section: Discussionmentioning

confidence: 99%

Toward greater consistency and validity in measuring interdisciplinarity: a systematic and conceptual evaluation

2022

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“…The TI value reflects the number of disciplines associated with the subject words, while the BET value reflects the central degree of the subject words in the disciplines. Mao et al quantitatively studied the knowledge diffusion model in interdisciplinary fields through knowledge memes, which overcomes the problem that the citation-based method only considers the number of citations but does not consider the real citation content (Mao et al, 2020). Kamada et al proposed the diffusion meme index to evaluate the knowledge diffusion distance in the citation network, which can be used to discover interdisciplinary research (Kamada et al, 2021).…”

Section: Related Workmentioning

confidence: 99%

Discovering Interdisciplinary Research Based on Neural Networks

et al. 2022

Front. Bioeng. Biotechnol.

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Interdisciplinary research promotes the emergence of scientific innovation. Researchers want to find interdisciplinary research in their research field. However, the number of scientific papers published today is increasing, and completing this task by hand is time-consuming and laborious. A neural network is a machine learning model that simulates the connection mode of neurons in the human brain. It is an important application of bionics in the artificial intelligence field. This paper proposes an approach to discovering interdisciplinary research automatically. The method generates an IRD-BERT neural network model for discovering interdisciplinary research based on the pre-trained model BERT. IRD-BERT is used to simulate the domain knowledge of experts, and author keywords can be projected into vector space by this model. According to the keyword distribution in the vector space, keywords with semantic anomalies can be identified. Papers that use these author keywords are likely to be interdisciplinary research. This method is applied to discover interdisciplinary research in the deep learning research field, and its performance is better than that of similar methods.

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“…The overlapping community algorithm was used to identify the intersections and potential intersections between disciplines in the co-word network (Li et al, 2013). Knowledge memes were introduced to quantify knowledge flow across disciplines from the novelty of content (Mao et al, 2020). The final extracted knowledge elements can better reflect the knowledge flow between papers, but there are also problems such as difficult extraction, complex topic division and the need to introduce too much subjective evaluation.…”

Section: Introductionmentioning

confidence: 99%

Measuring the interdisciplinary characteristics of Chinese research in library and information science based on knowledge elements

Zeng

Cao

Chen

et al. 2023

AJIM

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PurposeThis study analyzed the interdisciplinary characteristics of Chinese research studies in library and information science (LIS) measured by knowledge elements extracted through the Lexicon-LSTM model.Design/methodology/approachEight research themes were selected for experiment, with a large-scale (N = 11,625) dataset of research papers from the China National Knowledge Infrastructure (CNKI) database constructed. And it is complemented with multiple corpora. Knowledge elements were extracted through a Lexicon-LSTM model. A subject knowledge graph is constructed to support the searching and classification of knowledge elements. An interdisciplinary-weighted average citation index space was constructed for measuring the interdisciplinary characteristics and contributions based on knowledge elements.FindingsThe empirical research shows that the Lexicon-LSTM model has superiority in the accuracy of extracting knowledge elements. In the field of LIS, the interdisciplinary diversity indicator showed an upward trend from 2011 to 2021, while the disciplinary balance and difference indicators showed a downward trend. The knowledge elements of theory and methodology could be used to detect and measure the interdisciplinary characteristics and contributions.Originality/valueThe extraction of knowledge elements facilitates the discovery of semantic information embedded in academic papers. The knowledge elements were proved feasible for measuring the interdisciplinary characteristics and exploring the changes in the time sequence, which helps for overview the state of the arts and future development trend of the interdisciplinary of research theme in LIS.

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Quantifying cross-disciplinary knowledge flow from the perspective of content: Introducing an approach based on knowledge memes

Cited by 22 publications

References 54 publications

Toward greater consistency and validity in measuring interdisciplinarity: a systematic and conceptual evaluation

Toward greater consistency and validity in measuring interdisciplinarity: a systematic and conceptual evaluation

Discovering Interdisciplinary Research Based on Neural Networks

Measuring the interdisciplinary characteristics of Chinese research in library and information science based on knowledge elements

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