Technology Opportunity Analysis: Combining SAO Networks and Link Prediction

Han, Xiaotong; Zhu, Ding; Wang, Xuefeng; Li, Jia; Qiao, Yali

doi:10.1109/tem.2019.2939175

Cited by 25 publications

(2 citation statements)

References 39 publications

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“…Firstly, text analysis can be used to extract knowledge elements from the data and achieve dimensionality reduction in the data [ 32 ]. Secondly, cluster analysis [ 64 ], association analysis [ 95 ], modeling and simulation [ 33 ], and network analysis [ 73 ] can be used to further explore the logical relationships between elements and structured representation of elements. Again, the representation and definition of technological opportunities can be achieved with the help of expert experience or descriptive and matrix analysis methods [ 47 ].…”

Section: Resultsmentioning

confidence: 99%

A Review of Technological Forecasting from the Perspective of Complex Systems

Feng

Wang

et al. 2022

Entropy

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Technology forecasting (TF) is an important way to address technological innovation in fast-changing market environments and enhance the competitiveness of organizations in dynamic and complex environments. However, few studies have investigated the complex process problem of how to select the most appropriate forecasts for organizational characteristics. This paper attempts to fill this research gap by reviewing the TF literature based on a complex systems perspective. We first identify four contexts (technology opportunity identification, technology assessment, technology trend and evolutionary analysis, and others) involved in the systems of TF to indicate the research boundary of the system. Secondly, the four types of agents (field of analysis, object of analysis, data source, and approach) are explored to reveal the basic elements of the systems. Finally, the visualization of the interaction between multiple agents in full context and specific contexts is realized in the form of a network. The interaction relationship network illustrates how the subjects coordinate and cooperate to realize the TF context. Accordingly, we illustrate suggest five trends for future research: (1) refinement of the context; (2) optimization and expansion of the analysis field; (3) extension of the analysis object; (4) convergence and diversification of the data source; and (5) combination and optimization of the approach.

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Section: Resultsmentioning

confidence: 99%

A Review of Technological Forecasting from the Perspective of Complex Systems

Feng

Wang

et al. 2022

Entropy

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“…Technology opportunity analysis (TOA) helps researchers and organizations explore potential technological opportunities. It also enables a better understanding of scientific and technological developments by deeply mining valid information in publications, patents, and the literature [43].…”

Section: Technology Opportunity Analysismentioning

confidence: 99%

Data-Driven Technology Roadmaps to Identify Potential Technology Opportunities for Hyperuricemia Drugs

et al. 2022

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Hyperuricemia is a metabolic disease with an increasing incidence in recent years. It is critical to identify potential technology opportunities for hyperuricemia drugs to assist drug innovation. A technology roadmap (TRM) can efficiently integrate data analysis tools to track recent technology trends and identify potential technology opportunities. Therefore, this paper proposes a systematic data-driven TRM approach to identify potential technology opportunities for hyperuricemia drugs. This data-driven TRM includes the following three aspects: layer mapping, content mapping and opportunity finding. First we deal with layer mapping.. The BERT model is used to map the collected literature, patents and commercial hyperuricemia drugs data into the technology layer and market layer in TRM. The SAO model is then used to analyze the semantics of technology and market layer for hyperuricemia drugs. We then deal with content mapping. The BTM model is used to identify the core SAO component topics of hyperuricemia in technology and market dimensions. Finally, we consider opportunity finding. The link prediction model is used to identify potential technological opportunities for hyperuricemia drugs. This data-driven TRM effectively identifies potential technology opportunities for hyperuricemia drugs and suggests pathways to realize these opportunities. The results indicate that resurrecting the pseudogene of human uric acid oxidase and reducing the toxicity of small molecule drugs will be potential opportunities for hyperuricemia drugs. Based on the identified potential opportunities, comparing the DNA sequences from different sources and discovering the critical amino acid site that affects enzyme activity will be helpful in realizing these opportunities. Therefore, this research provides an attractive option analysis technology opportunity for hyperuricemia drugs.

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