Identification of interdisciplinary ideas

Thorleuchter, Dirk; Poel, Dirk Van den

doi:10.1016/j.ipm.2016.04.010

Cited by 14 publications

(2 citation statements)

References 26 publications

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“…In this context, disciplines are defined as aggregators of scientific ideas and concepts are particularly relevant for the holistic understanding of scientific texts. This happens at different levels of granularity and with either top-down (supervised) and bottomup (semi-supervised) approaches to the recognition and classification of disciplinary lexicon (Wang et al, 2013;Nichols, 2014;Silva et al, 2016;Thorleuchter and Van den Poel, 2016;Xu et al, 2018;Bu et al, 2021;Kelly et al, 2021;Kim et al, 2022).…”

Section: Methods Of Semantic Classification and Surveysmentioning

confidence: 99%

How to measure Interdisciplinary Research? A `systemic' design for the model of measurement

Cantone

2024

Preprint

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Interdisciplinary research is defined as a trait of those research activities that integrate different disciplinary traditions with the aim of reaching novel forms of knowledge that are beyond the reach of a singular discipline. From this theory, a plurality of methods has been developed to measure IDR of a body of research. These methods lead to mutually contradicting results. In this review, the most relevant measures of interdisciplinary research are compared under a unified framework articulated in three steps: identification of an available stylisation of bibliometric facts, elicitation of a formula, and definition of an estimator for the collective unit of analysis.In particular, it is discussed how the concept of integration can be measured when the unit of analysis is an atomic and static entity like a paper, as opposed to journals and authors, which are collective and dynamic agents. In the conclusive section, an isometric view is shown that combines different units of analysis and conceptualisations. A pluralistic yet pragmatic approach is advocated for the operational definition of interdisciplinarity.

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Section: Methods Of Semantic Classification and Surveysmentioning

confidence: 99%

How to measure Interdisciplinary Research? A `systemic' design for the model of measurement

Cantone

2024

Preprint

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“…The idea mining technique proposed by [4] was used to generate ideas, and clustering using latent semantic indexing was used to create semantic clusters, and finally, to classify concepts, Jaccard's similarity was also used to identify interdisciplinary idea G [90] Social media Idea generation through the combination of topic modeling, LDA, and sentiment analysis to measure satisfaction…”

Section: Summary Of Identified Idea Generation Techniques Using Idea ...mentioning

confidence: 99%

A Systematic Literature Review about Idea Mining: The Use of Machine-Driven Analytics to Generate Ideas

Ayele

Juell-Skielse

2021

Advances in Intelligent Systems and Computing

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Idea generation is the core activity of innovation. Digital data sources, which are sources of innovation, such as patents, publications, social media, websites, etc., are increasingly growing at unprecedented volume. Manual idea generation is time-consuming and is affected by the subjectivity of the individuals involved. Therefore, the use machine-driven data analytics techniques to analyze data to generate ideas and support idea generation by serving users is useful. The objective of this study is to study state-of the-art machine-driven analytics for idea generation and data sources, hence the result of this study will generally serve as a guideline for choosing techniques and data sources. A systematic literature review is conducted to identify relevant scholarly literature from IEEE, Scopus, Web of Science and Google Scholar. We selected a total of 71 articles and analyzed them thematically. The results of this study indicate that idea generation through machine-driven analytics applies text mining, information retrieval (IR), artificial intelligence (AI), deep learning, machine learning, statistical techniques, natural language processing (NLP), NLP-based morphological analysis, network analysis, and bibliometric to support idea generation. The results include a list of techniques and procedures in idea generation through machine-driven idea analytics. Additionally, characterization and heuristics used in idea generation are summarized. For the future, tools designed to generate ideas could be explored.

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Knowledge Integration: Its Meaning and Measurement

Rousseau

Zhang

2019

Springer Handbooks

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Identification of interdisciplinary ideas

Cited by 14 publications

References 26 publications

How to measure Interdisciplinary Research? A `systemic' design for the model of measurement

How to measure Interdisciplinary Research? A `systemic' design for the model of measurement

A Systematic Literature Review about Idea Mining: The Use of Machine-Driven Analytics to Generate Ideas

Knowledge Integration: Its Meaning and Measurement

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