Measuring the innovation of method knowledge elements in scientific literature

Wang, Zhongyi; Wang, Keying; Liu, Jiyue; Jing, Huang; Chen, Haihua

doi:10.1007/s11192-022-04350-5

Cited by 11 publications

(10 citation statements)

References 54 publications

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“…Chinese LIS research Jarvelin and Vakkari (1990) extracted research methods in academic papers using content analysis, and Pan et al (2015) adopted a bootstrapped learning method to automatically extract software entities. Some knowledge entity studies have used research method extraction algorithms, software and datasets to evaluate the influence of knowledge elements (Tuarob et al, 2016;Wang and Zhang, 2020;Wang et al, 2022).…”

Section: Knowledge Extraction Inmentioning

confidence: 99%

“…3.2.2 Baselines. For comparison, several representative models were selected as baselines on the dataset used in this paper, including W2V-BiLSTM-CRF, Glove-BiLSTM-CRF, Text-CNN, SCI-BERT, and Rule-based method by Wang et al (2022). Among them, W2V-BiLSTM-CRF and Glove-BiLSTM-CRF use LSTM as the feature extractor, biphasic recurrent neural networks to combine pre and post-text related information, and CRF as the output layer, which is a CRF commonly used in NLP tasks to solve sequence annotation problems.…”

Section: Knowledge Extraction In Chinese Lis Researchmentioning

confidence: 99%

“…Some knowledge entity studies have used research method extraction algorithms, software and datasets to evaluate the influence of knowledge elements (Tuarob et al. , 2016; Wang and Zhang, 2020; Wang et al. , 2022).…”

Section: Related Workmentioning

confidence: 99%

“…While content analysis based on topic identification is more about measuring potential knowledge and combining multi-dimensional information such as subject categories, chapter information and specific terms, contents demonstrate contributions to knowledge systems more directly than citations (Wang et al, 2022). The overlapping community algorithm was used to identify the intersections and potential intersections between disciplines in the co-word network (Li et al, 2013).…”

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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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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