Interdisciplinary topics of information science: a study based on the terms interdisciplinarity index series

Xu, Haiyun; Guo, Tao; Yue, Zenghui; Ru, Lijie; Fang, Shu

doi:10.1007/s11192-015-1792-2

Cited by 36 publications

(26 citation statements)

References 15 publications

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“…The first method involves an index series named terms interdisciplinarity (TI index), which attempts to recognize topics by calculating TI values together with Bet values and term frequency values and analyzes the evolution of interdisciplinary sciences based on social network analysis and time-series analysis. 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%

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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%

“…Leydesdorff et al (2013) visually displayed both the citing and cited interdisciplinary citation matrices using Rao-Stirling indicators. Xu et al (2016) introduced a measurement index called topic terms interdisciplinarity (TI) for IDR topic mining. They showed that the TI value can identify IDR topic terms effectively.…”

Section: Idr Measurement Indexmentioning

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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“…Unlike other indexes, it overcomes at least partially the issue of an arbitrary choice of a predefined categorization (Cassi et al, 2014). Therefore, Stirling's diversity index is more systematic or robust for measuring interdisciplinarity than other indexes (Stirling, 2007;Leydesdorff and Rafols, 2011), such as Shannon's index (Rafols and Meyer, 2010;Karlovcec and Mladenic, 2015), or TF-IDF (Xu et al, 2016). Second, researchers have used social network analysis to calculate measurements of interdisciplinarity, such as network coherence or a similar network of publications (Rafols and Meyer, 2010), degree centrality of a co-occurrence network among disciplines, and betweenness centrality of the citation network among journals (Leydesdorff, 2007;Leydesdorff and Rafols, 2011).…”

Section: Bibliometric Studies On Interdisciplinaritymentioning

confidence: 99%

Measuring the interdisciplinarity of Big Data research: a longitudinal study

Yin

2018

OIR

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Purpose The purpose of this paper is to measure the degree of interdisciplinary collaboration in Big Data research based on the co-occurrences of subject categories using Stirling’s diversity index and specialization index. Design/methodology/approach Interdisciplinarity was measured utilizing the descriptive statistics of disciplines, network indicators showing relationships between disciplines and within individual disciplines, interdisciplinary communities, Stirling’s diversity index and specialization index, and a strategic diagram revealing the development status and trends of discipline communities. Findings Comprehensively considering all results, the degree of interdisciplinarity of Big Data research is increasing over time, particularly, after 2013. There is a high level of interdisciplinarity in Big Data research involving a large number of disciplines, but it is unbalanced in distribution. The interdisciplinary collaborations are not intensive on the whole; most disciplines are aggregated into a few distinct communities with computer science, business and economics, mathematics, and biotechnology and applied microbiology as the core. Four major discipline communities in Big Data research represent different directions with different development statuses and trends. Community 1, with computer science as the core, is the most mature and central to the whole interdisciplinary network. Accounting for all network indicators, computer science, engineering, business and economics, social sciences, and mathematics are the most important disciplines in Big Data research. Originality/value This study deepens our understanding of the degree and trend of interdisciplinary collaboration in Big Data research through a longitudinal study and quantitative measures based on two indexes. It has practical implications to study and reveal the interdisciplinary phenomenon and characteristics of related developments of a specific research area, or to conduct comparative studies between different research areas.

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“…Based on published papers , researchers investigated interdisciplinarity in the following research areas : chemistry [ 9 ], physics [ 10 ], nanoscience and nanotechnology[ 11 – 13 ], biotechnology [ 14 ], information science and library science [ 15 – 18 ], humanities [ 19 ], bionanoscience [ 3 , 20 ], Japanese rice research and technology development [ 21 ], biochemistry and molecular biology [ 22 ], medical science [ 23 ], and biotechnology [ 24 ]. The problem regarding studies based on published papers is the incomplete collection of bibliographic data, although this issue has recently been addressed from a technical perspective [ 25 ].…”

Section: Introductionmentioning

confidence: 99%

Interdisciplinarity research based on NSFC-sponsored projects: A case study of mathematics in Chinese universities

Shao

Hui

et al. 2018

PLoS ONE

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We investigate the interdisciplinarity of mathematics based on an analysis of projects sponsored by the NSFC (National Natural Science Foundation of China). The motivation of this study lies in obtaining an efficient method to quantify the research interdisciplinarities, revealing the research interdisciplinarity patterns of mathematics discipline, giving insights for mathematics scholars to improve their research, and providing empirical supports for policy making. Our data set includes 6147 NSFC-sponsored projects implemented by 3225 mathematics professors in 177 Chinese universities with established mathematics departments. We propose the weighted-mean DIRD (diversity of individual research disciplines) to quantify interdisciplinarity. In addition, we introduce the matrix computation method, discover several properties of such a matrix, and make the computation cost significantly lower than the bitwise computation method. Finally, we develop an automatic DIRD computing system. The results indicate that mathematics professors at top normal universities in China exhibit strong interdisciplinarity; mathematics professors are most likely to conduct interdisciplinary research involving information science (research department), computer science (research area), computer application technology (research field), and power system bifurcation and chaos (research direction).

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Interdisciplinary topics of information science: a study based on the terms interdisciplinarity index series

Cited by 36 publications

References 15 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

Measuring the interdisciplinarity of Big Data research: a longitudinal study

Interdisciplinarity research based on NSFC-sponsored projects: A case study of mathematics in Chinese universities

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