Technology opportunity discovery based on constructing, evaluating, and searching knowledge networks

Ren, Haiying; Zhao, Yuhui

doi:10.1016/j.technovation.2020.102196

Cited by 29 publications

(6 citation statements)

References 69 publications

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“…Information scientists leapt at the opportunity to apply network analytics to explore insights from network topologies (Björneborn, 2004). Previous bibliometric network analytics have: (a) used topological indicators (e.g., centrality) to identify key nodes, for example, influential researchers in a co-authorship network (Li et al, 2013;Yan & Ding, 2009); (b) used topology-based approaches (e.g., community detection and link prediction) to recognize specific behaviors and patterns, for example, collaborations (Yan & Guns, 2014), disciplinary interactions (Huang et al, 2020), and problemsolving patterns (Zhang, Wu, Hu, et al, 2021a); and (c) connected bibliometric networks with broad ST&I paradigms, for example, technology roadmaps (Jeong et al, 2021) and technology opportunity analysis (Ren & Zhao, 2021). Sun and Han (2012) argued, "the interactions among multi-typed objects play a key role in disclosing the rich semantics that a network carries" and defined a meta path as sequential links between any two entities in a heterogeneous network.…”

Section: Bibliometric Network Analyticsmentioning

confidence: 99%

Stepping beyond your comfort zone: Diffusion‐based network analytics for knowledge trajectory recommendation

Zhang

et al. 2023

Asso for Info Science & Tech

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Predicting a researcher's knowledge trajectories beyond their current foci can leverage potential inter‐/cross‐/multi‐disciplinary interactions to achieve exploratory innovation. In this study, we present a method of diffusion‐based network analytics for knowledge trajectory recommendation. The method begins by constructing a heterogeneous bibliometric network consisting of a co‐topic layer and a co‐authorship layer. A novel link prediction approach with a diffusion strategy is then used to capture the interactions between social elements (e.g., collaboration) and knowledge elements (e.g., technological similarity) in the process of exploratory innovation. This diffusion strategy differentiates the interactions occurring among homogeneous and heterogeneous nodes in the heterogeneous bibliometric network and weights the strengths of these interactions. Two sets of experiments—one with a local dataset and the other with a global dataset—demonstrate that the proposed method is prior to 10 selected baselines in link prediction, recommender systems, and upstream graph representation learning. A case study recommending knowledge trajectories of information scientists with topical hierarchy and explainable mediators reveals the proposed method's reliability and potential practical uses in broad scenarios.

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Section: Bibliometric Network Analyticsmentioning

confidence: 99%

Stepping beyond your comfort zone: Diffusion‐based network analytics for knowledge trajectory recommendation

Zhang

et al. 2023

Asso for Info Science & Tech

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“…Similarities exist in the new product development innovation process within the stages of identifying issues and opportunities, creating and processing ideas, market projections, business analysis, visualization, and execution, as well as expressing the model in service organizations [20]. The current research contains various outstanding challenges concerning the discovery of technology opportunities [21]. Only an appropriate product structure will allow the benefits of product portfolio management to be fully realized [22].…”

Section: Literature Reviewmentioning

confidence: 99%

Selection of Unmanned Aircraft Development Model in Indonesia using the AHP Method

Utama,

Sumantri,

Bura

et al. 2023

IJACSA

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Countries worldwide are attempting to acquire or create Class 3 unmanned aircraft as part of their armies' primary weapons systems. The development of medium altitude long endurance (MALE) unmanned aircraft in Indonesia forms part of the national strategic program. Based on documentation studies, three alternative MALE-class unmanned aircraft development models were identified. This study aims to determine the most appropriate unmanned aircraft development model for the MALE class for Indonesia's current situation. This will aid decision-making by the government and stakeholders related to the drone development model. The analytical hierarchy process (AHP) method was used to analyze the decision-making for the selection of an unmanned aircraft development model. The study began with a questionnaire survey of 11 experts from various institutions. The results show that the priority criterion should be the benefits obtained, followed by the opportunity and budget criteria, and, finally, the risk. The consortium model, which had the highest score of 0.548, is the most suitable for Indonesia's development of MALE-class unmanned aircraft. The results of the study are expected to provide useful input for AHP researchers, government institutions, and stakeholders.

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“…For example, You et al (2021) introduced deep learning to evaluate patent quality. Also, Ren & Zhao (2021) applied regression analysis and heuristic algorithms to patent-based technology opportunity discovery. These studies demonstrate the potential of data-driven S&T evaluation methods to effectively evaluate and discover new opportunities in real time.…”

Section: Grasping Digital-driven Paradigm Shift In Sandt Evaluationmentioning

confidence: 99%

New directions of digitally driven S&T evaluation

2023

DSI

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Science and Technology (S&T) evaluation plays a baton role in developing science and technology innovation. However, traditional S&T evaluation indicators and methods are difficult to apply effectively in S&T evaluation practice. This paper analyzes the transformation of the S&T evaluation paradigm in the digital environment. Theories, methods, and tools of S&T evaluation research are continuously innovated and optimized; big data becomes the driving force of S&T evaluation development; the role played by S&T evaluation is shifting from a provider of statistical data and information to a participant in S&T decision-making activities.S&T evaluation research should focus on improving data retrieval and organization, knowledge mining and knowledge discovery, and intelligent evaluation models. Moreover, we suggest that scientists carry out S&T evaluation in agreement with the needs of S&T development: 1) monitoring and sensing the development of science and technology in real-time with the help of emerging digital technologies; 2) exploring solutions to major concerns such as technical project management mechanisms, utilizing advanced data science and digital technologies to identify important scientific frontiers, and leveraging big data in science of science to reveal patterns and characteristics of scientific structures and activities; 3) carrying out problem-oriented evaluation research practice focused on four aspects, including intelligent project evaluation, evaluation of the critical technology competitiveness, talent assessment, and diagnostic evaluation of the research entity competitiveness.

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Technology opportunity discovery based on constructing, evaluating, and searching knowledge networks

Cited by 29 publications

References 69 publications

Stepping beyond your comfort zone: Diffusion‐based network analytics for knowledge trajectory recommendation

Stepping beyond your comfort zone: Diffusion‐based network analytics for knowledge trajectory recommendation

Selection of Unmanned Aircraft Development Model in Indonesia using the AHP Method

New directions of digitally driven S&T evaluation

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