Abstract
Interdisciplinary research has attracted extensive attention from researchers and policymakers by its nature of integrating various types of knowledge from multiple disciplines to solve complex scientific problems. Besides the studies on citation-based interdisciplinary knowledge flow, recent efforts have been made to demystify the characteristics of knowledge integration in interdisciplinary research from a knowledge content perspective. To deeply understand the knowledge content integrated into interdisciplinary research, two tasks were formulated in this study. One was to identify the knowledge units integrated by an interdisciplinary field, which are defined as integrated knowledge phrases (IKPs) shared between citances and cited texts of the references. The other was to classify the identified IKPs into several knowledge categories, which could reflect their knowledge functions in the field. We proposed a methodology framework to automate the identification and classification of IKPs by using natural language processing techniques and deep learning models. This automatic methodology was tested on an eHealth dataset. The experiments showed that the baseline matching method and the word embedding based similarity matching method are effective for the identification task, and the Bidirectional Encoder Representation from Transformers (BERT) model using section titles and citances as input features achieved the best performance on the classification task, with an accuracy of 0.951. We further showcased the application of IKPs in the case study with expanded literature of eHealth. The two tasks were operated on the new dataset, then co-occurrence networks of IKPs were constructed and mapped to visualize the knowledge integration structure of the field. This study provides a feasible content-based methodology to foster the fine-grained understanding of the knowledge integration structure of an interdisciplinary field, which could become a general domain analysis method.
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Acknowledgements
This study was funded by the National Social Science Foundation of China with Grant No. 20CTQ024. This article is an extension of our work presented at the 1st Workshop on AI + Informetrics (AII2021) at the iConference2021 [https://www.ncbi.nlm.nih.gov/pmc/tools/openftlist/, https://www.aminer.cn/, https://ai-informetrics.github.io/].
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Appendices
Appendix A
Ten clusters in the 2009 IKPs co-occurrence network.
Cluster | Number of IKPs | Representative IKPs and their knowledge categories |
|---|---|---|
1 | 35 | health care (Research Subject); health inform technolog (Technology); electron health record (Technology); medic informat (Research Subject); evalu studi (Research Methodology) |
2 | 34 | physic activ (Research Subject); primari care (Research Subject); primari care physician (Entity); bodi mass index (Research Methodology); real life set (Research Subject) |
3 | 32 | health inform (Research Subject); cancer survivor (Entity); Internet use (Research Subject); onlin cancer support group (Entity); african american (Entity) |
4 | 28 | emerg depart (Research Subject); patient safeti (Research Subject); heurist evalu (Research Methodology); medic error (Research Suject); intens case unit (Research Subject) |
5 | 20 | systemat review (Research Methodology); clinic practic guidelin (Research Subject); healthcar profession (Entity); chronic diseas (Research Subject); inform system (Technology) |
6 | 19 | breast cancer (Research Subject); gene express (Research Subject); earli detect (Research Subject); alcohol consumpt (Research Subject); self esteem (Research Subject) |
7 | 15 | social support (Research Subject); content analysi (Research Methodology); gender differ (Research Subject); mental health (Research Subject); decis support (Research Subject) |
8 | 13 | random control trial (Research Methodology); help seek (Research Subject); profession help (Research Subject); help seek process (Research Subject); increas help (Research Subject) |
9 | 13 | public health (Research Subject); infecti diseas (Research Subject); medic provid (Entity); social medium (Technology); relev infecti diseas (Research Subject) |
10 | 5 | control group (Entity); math comput perform (Research Subject); comput studi guid (Research Subject); fewer substant error (Research Subject); text read task (Research Subject) |
Appendix B
Eight clusters in the 2014 IKPs co-occurrence network.
Cluster | Number of IKPs | Representative IKPs and their knowledge categories |
|---|---|---|
1 | 48 | health care (Research Subject); health inform (Research Subject); social medium (Technology); health literaci (Research Subject); ehealth literaci (Research Subject) |
2 | 47 | physic activ (Research Subject); behavior chang (Research Subject); weight loss (Research Subject); text messag (Technology); mobil phone (Technology) |
3 | 28 | breast cancer (Research Suject); cardiovascular diseas (Research Subject); social support (Research Subject); heart failur (Research Subject); risk factor (Research Subject) |
4 | 22 | self manag (Research Subject); self efficaci (Research Subject); patient activ (Research Subject); health promot (Research Subject); sexual health (Research Subject) |
5 | 15 | mental health (Research Subject); young peopl (Entity); mental health problem (Research Subject); young adult (Entity); mental disord (Research Subject) |
6 | 14 | systemat review (Research Methodology); meta analysi (Research Methodology); prefer report item (Research Methodology); blood pressur (Research Subject); decis support (Research Subject) |
7 | 9 | decis make (Research Subject); decis aid (Research Subject); share decis make (Theory); decision conflict scale (Research Methodology); patient decis aid (Research Subject) |
8 | 7 | random control trial (Research Methodology); control trial (Research Methodology); primari care (Research Subject); primari care physician (Entity); primari care set (Research Subject) |
Appendix C
Eleven clusters in the 2019 IKPs co-occurrence network.
Cluster | Number of IKPs | Representative IKPs and their knowledge categories |
|---|---|---|
1 | 71 | physic activ (Research Subject); behavior chang (Research Subject); weight loss (Research Subject); self monitor (Research Subject); self efficaci (Research Subject) |
2 | 48 | health care (Research Subject); social medium (Technology); patient portal (Technology); health outcom (Research Subject); health inform technolog (Technology) |
3 | 44 | systemat review (Research Methodology); meta analysi (Research Methodology); prefer report item (Research Methodology); mental health (Research Subject); control trial (Research Methodology) |
4 | 32 | blood pressur (Research Subject); risk factor (Research Subject); cardiovascular diseas (Research Subject); hear rate (Research Subject); energi expenditur (Research Subject) |
5 | 26 | primari care (Research Subject); electron health record (Technology); primari care set (Research Subject); clinic practic research datalink (Research Subject); uk popul (Entity) |
6 | 25 | breast cancer (Research Subject); cancer survivor (Entity); decis aid (Research Subject); breast cancer survivor (Entity); decis make (Research Subject) |
7 | 24 | health inform (Research Subject); ehealth literaci (Research Subject); health literaci (Research Subject); electron sourc (Research Subject); health problem (Research Subject) |
8 | 23 | self manag (Research Subject); chronic diseas (Research Subject); mobil app (Technology); mobil phone (Technology); chronic condit (Research Subject) |
9 | 17 | machin learn (Research Methodology); deep learn (Research Methodology); neural network (Research Methodology); random forest (Research Methodology); vector machin (Research Methodology) |
10 | 12 | text messag (Technology); smoke cessat (Research Subject); text messag intervent (Research Subject); medic remind (Research Subject); diseas manag (Research Subject) |
11 | 7 | atrial fibril (Research Subject); kidney diseas (Research Subject); chronic kidney diseas (Research Subject); heart failur (Research Subject); chronic obstruct pulmonari diseas (Research Subject) |
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Wang, S., Mao, J., Cao, Y. et al. Integrated knowledge content in an interdisciplinary field: identification, classification, and application. Scientometrics 127, 6581–6614 (2022). https://doi.org/10.1007/s11192-022-04282-0
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DOI: https://doi.org/10.1007/s11192-022-04282-0