An integrated method for interdisciplinary topic identification and prediction: a case study on information science and library science

Dong, Kun; Xu, Haiyun; Luo, Rui; Ling, Wei; Fang, Shu

doi:10.1007/s11192-018-2694-x

Cited by 18 publications

(5 citation statements)

References 12 publications

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“…A study has proved that the TI value can identify IDR topic terms well (Xu et al,2016). The second method is an integrated method for IDR topic recognition and prediction, which integrates various methods, including co-occurrence networks analysis, high-TI terms analysis, and burst detection, and offers an overall perspective into interdisciplinary topic identification (Dong et al, 2018).…”

Section: Comparative Analysis and Discussionmentioning

confidence: 99%

Identification and Prediction of Interdisciplinary Research Topics: A Study Based on the Concept Lattice Theory

Wang

Dong

et al. 2019

Journal of Data and Information Science

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Purpose: Formal concept analysis (FCA) and concept lattice theory (CLT) are introduced for constructing a network of IDR topics and for evaluating their effectiveness for knowledge structure exploration.Design/methodology/approach: We introduced the theory and applications of FCA and CLT, and then proposed a method for interdisciplinary knowledge discovery based on CLT. As an example of empirical analysis, interdisciplinary research (IDR) topics in Information & Library Science (LIS) and Medical Informatics, and in LIS and Geography-Physical, were utilized as empirical fields. Subsequently, we carried out a comparative analysis with two other IDR topic recognition methods. Findings:The CLT approach is suitable for IDR topic identification and predictions.Research limitations: IDR topic recognition based on the CLT is not sensitive to the interdisciplinarity of topic terms, since the data can only reflect whether there is a relationship between the discipline and the topic terms. Moreover, the CLT cannot clearly represent a large amounts of concepts. Practical implications:A deeper understanding of the IDR topics was obtained as the structural and hierarchical relationships between them were identified, which can help to get more precise identification and prediction to IDR topics.Originality/value: IDR topics identification based on CLT have performed well and this theory has several advantages for identifying and predicting IDR topics. First, in a concept lattice, there is a partial order relation between interconnected nodes, and consequently, a complete concept Citation: lattice can present hierarchical properties. Second, clustering analysis of IDR topics based on concept lattices can yield clusters that highlight the essential knowledge features and help display the semantic relationship between different IDR topics. Furthermore, the Hasse diagram automatically displays all the IDR topics associated with the different disciplines, thus forming clusters of specific concepts and visually retaining and presenting the associations of IDR topics through multiple inheritance relationships between the concepts. This paper is organized as follows. First, various methods and existing problems of IDR topic identification research are reviewed, and the characteristics of different methods used for IDR topic analysis are clarified. Then, the theory and application of FCA and CLT are introduced, and the method for identifying and predicting IDR topics based on the CLT is described. Subsequently, IDR topics in LIS & Medical Informatics, and in LIS & Geography-Physical, were utilized as empirical fields. A comparative analysis with two other IDR topic recognition methods is performed. Finally, the advantages and limitations of this study are discussed. 65Haiyun Xu et al.

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Section: Comparative Analysis and Discussionmentioning

confidence: 99%

Identification and Prediction of Interdisciplinary Research Topics: A Study Based on the Concept Lattice Theory

Wang

Dong

et al. 2019

Journal of Data and Information Science

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“…Research on discovering interdisciplinary research can be divided into two categories. One category is retrospective research, which identifies and analyses the topics in papers when it is known that these papers are interdisciplinary (Wu et al, 2017;Dong et al, 2018). This kind of research is unable to address the problems that need to be solved in this paper.…”

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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“…Some research, however, explored keywords, term analysis and topic model approach in measuring interdisciplinarity (e.g., Ba et al, 2019;Nichols, 2014;Wang et al, 2013;Xu et al, 2016). Dong et al (2018) offer an integrated method (based on co-occurrence networks and the term interdisciplinarity) for identifying and predicting interdisciplinary topics from scientific literature. At present, most lexical approaches are local solutions (in subject-related terms), but also in this context hybrid citation-lexical solutions are possible to improve efficiency -at least at the local level (see Thijs, 2020).…”

Section: Quantification and Measurementmentioning

confidence: 99%

Various aspects of interdisciplinarity in research and how to quantify and measure those

Glänzel

Debackere

2021

Scientometrics

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Interdisciplinary research figures high on today's policy agendas. This short introduction and overview sketches the complexity of defining and mapping the nature of interdisciplinary research (IDR). The paper focuses on the different approaches to IDR and different methods applied in bibliometric studies that allow measuring it. These methods should not only be able to capture quantitative aspects of IDR but also to monitor evolutionary aspects and help answer the question of whether IDR stimulates collaboration and results in larger impact and visibility. Two specific indicators, variety and disparity, are developed, validated and applied to bibliometric data. They enable the visualization of the interdisciplinary nature of research activities at various levels of analysis (both institutional and individual). And, given the longitudinal character of bibliometric data and databases, both indicators allow for mapping time-dependent phenomena and evolutions. Relevant examples based on the literature and recent results from research conducted at the Leuven bibliometrics group of ECOOM (e.g., Glänzel et al., 2021;Huang et al., 2021) are given, and concrete proposals for future research are articulated.

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An integrated method for interdisciplinary topic identification and prediction: a case study on information science and library science

Cited by 18 publications

References 12 publications

Identification and Prediction of Interdisciplinary Research Topics: A Study Based on the Concept Lattice Theory

Identification and Prediction of Interdisciplinary Research Topics: A Study Based on the Concept Lattice Theory

Discovering Interdisciplinary Research Based on Neural Networks

Various aspects of interdisciplinarity in research and how to quantify and measure those

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