Identifying technology evolution pathways using topic variation detection based on patent data: A case study of 3D printing

Miao, Zhongzhen; Du, Jun-fei; Dong, Fang; Liu, Yuanyuan; Wang, Xiaochuan

doi:10.1016/j.futures.2020.102530

Cited by 23 publications

(10 citation statements)

References 31 publications

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“…Yoon et al constructed a semantic network using text mining methods to analyze the development trend of technology [ 33 ]. Miao et al has studied more than 30,000 patents since the 1990s using text mining methods to obtain products and applications with application prospects and rule out traditional technologies with a declining trend [ 11 ]. However, text mining methods pay more attention to the semantic information carried by patent text while ignoring the relationship between patents.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Essentially, the knowledge graph is a semantic network with nodes and edges that reveals the entities and relationships and can formally describe things and relationships in the real world. In addition, at the level of research purpose, scholars only focused on the technology evolution pathways over time [ 11 , 12 , 13 , 14 , 15 , 16 , 17 , 18 , 19 ], which ignored the progressive evolutionary process from scientific grants to academic papers and then to patents, while technologies generally emerge with grants or papers and become sophisticated with patents.…”

Section: Introductionmentioning

confidence: 99%

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Advanced Technology Evolution Pathways of Nanogenerators: A Novel Framework Based on Multi-Source Data and Knowledge Graph

et al. 2022

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As an emerging nano energy technology, nanogenerators have been developed rapidly, which makes it crucial to analyze the evolutionary pathways of advanced technology in this field to help estimate the development trend and direction. However, some limitations existed in previous studies. On the one hand, previous studies generally made use of the explicit correlation of data such as citation and cooperation between patents and papers, which ignored the rich semantic information contained in them. On the other hand, the progressive evolutionary process from scientific grants to academic papers and then to patents was not considered. Therefore, this paper proposes a novel framework based on a separated three-layer knowledge graph with several time slices using grant data, paper data, and patent data. Firstly, by the representation learning method and clustering algorithm, several clusters representing specific technologies in different layers and different time slices can be obtained. Then, by calculating the similarity between clusters of different layers, the evolutionary pathways of advanced technology from grants to papers and then to patents is drawn. Finally, this paper monitors the pathways of some developed technologies, which evolve from grants to papers and then to patents, and finds some emerging technologies under research.

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Section: Literature Reviewmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Advanced Technology Evolution Pathways of Nanogenerators: A Novel Framework Based on Multi-Source Data and Knowledge Graph

et al. 2022

Self Cite

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“…After that, Chen et al mined the patent abstract to track the evolution trend and identified the potential R&D hotspots of 3D printing technology [53]. Miao et al drew the evolution pathway of 3D printing technology in different time periods, and the results showed that the traditional technologies of 3D printing follow a declining trend, while the practical application of it is a promising direction [67].…”

Section: D Printing Technologymentioning

confidence: 99%

The Evolution of Interindustry Technology Linkage Topics and Its Analysis Framework in Three-Dimensional Printing Technology

Yang

Islam

Shi

et al. 2023

IEEE Trans. Eng. Manage.

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The mutual influence and complementarity of technologies between different industries are becoming increasingly prominent. Revealing the topic evolution of technology linkages between industries is the foundation for understanding the technological development trend of the industry. Although numerous works have focused on technology topic mining and its evolution characteristics, these works have not accurately represented the interindustry technology linkage, analyze the related topics and even ignored the technological development characteristics hidden in the topic evolution pathway. Since the Lingo algorithm fully considers the time-series characteristics of the topics, and the knowledge evolution theory can reveal three inherent characteristics in the evolution of knowledge topics, namely, "stability, heredity and variability," this paper aims to combine the Lingo algorithm and the knowledge evolution theory to analyze the topic evolution of interindustry technology linkages. Additionally, because 3D printing technology has significant interdisciplinary and cross-industry characteristics, a wide range of application fields, and various interindustry technology linkages, 3D printing technology is used for empirical analysis. The empirical results show that the key topics of interindustry technology linkages in 3D printing include model design, manufacturing methods, manufacturing equipment, manufacturing material and application. In addition, all these topics have the development feature of heredity.However, the topic of manufacturing materials presents significant variability, the topic of manufacturing methods has the strongest stability, and multiple subtopics of the five topics show variability and genetic intersection.

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“…Different from the topic content evolution, evolution path analysis aims to present topic drifting. Based on definite cases, some scholars studied the evolution path in the field of technology (Jung & Yoon, 2020 ; Miao et al, 2020 ; Wu et al, 2014 ; Xu et al, 2020 ). For example, Chen et al ( 2018 ) proposed a method that combines LDA and hidden markov model (HMM) to mine the topics of patents, and then explored the topic intensity and the evolution path between topics.…”

Section: Related Workmentioning

confidence: 99%

Evolution analysis of online topics based on ‘word-topic’ coupling network

Zhu

Qin

et al. 2022

Scientometrics

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Analyzing topic evolution is an effective way to monitor the overview of topic spreading. Existing methods have focused either on the intensity evolution of topics along a timeline or the topic evolution path of technical literature. In this paper, we aim to study topic evolution from a micro perspective, which not only captures the topic timeline but also reveals the topic status and the directed evolutionary path among topics. Firstly, we construct a word network by co-occurrence relationship between feature words. Secondly, Latent Dirichlet allocation (LDA) model is used to automatically extract topics and capture the mapping relationship between words and topics, and then a ‘word-topic’ coupling network is built. Thirdly, based on the ‘word-topic’ coupling network, we describe the topic intensity evolution over time and measure topic status considering the contribution of feature words to a topic. The concept of topic drifting probability is proposed to identify the evolutionary path. Experimental results conducted on two real-world data sets of “COVID-19” demonstrate the effectiveness of our proposed method.

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Identifying technology evolution pathways using topic variation detection based on patent data: A case study of 3D printing

Cited by 23 publications

References 31 publications

Advanced Technology Evolution Pathways of Nanogenerators: A Novel Framework Based on Multi-Source Data and Knowledge Graph

Advanced Technology Evolution Pathways of Nanogenerators: A Novel Framework Based on Multi-Source Data and Knowledge Graph

The Evolution of Interindustry Technology Linkage Topics and Its Analysis Framework in Three-Dimensional Printing Technology

Evolution analysis of online topics based on ‘word-topic’ coupling network

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