Measuring knowledge exploration distance at the patent level: Application of network embedding and citation analysis

Choi, Jin Sik; Yoon, Janghyeok

doi:10.1016/j.joi.2022.101286

Cited by 18 publications

(2 citation statements)

References 37 publications

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“…Zhang et al [4] combined bibliometrics, qualitative analysis, and visualization techniques to build a hybrid model for writing technology roadmaps. Choi et al [27] used network embedding and citation analysis to vectorize patents, and they calculated the distance of patents in the vector space to indicate the degree of correlation between patents. The other approach is through methods based on semantic mining and feature extraction.…”

Section: Text Miningmentioning

confidence: 99%

Exploring the Evolution of Core Technologies in Agricultural Machinery: A Patent-Based Semantic Mining Analysis

Wei,

Jiang,

Feng

et al. 2023

Electronics

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The semi-automatic construction and analysis of technology roadmaps are at the forefront of applying artificial intelligence techniques. To clarify the development path of the core technologies in the field of agricultural machinery, we propose a core technology evolution path analysis method based on patent text semantic mining. First, the key sentences in the text were extracted, and the BERT model was used to represent the topic’s key sentences in semantic vectorization. Then, the technology roadmap was constructed through the unsupervised LDA topic clustering method, and the main fields of activity, blank fields, and fields of agricultural machinery were visually analyzed. Next, investment in research fields was strengthened. Finally, we mapped the technology roadmap and patent IPC codes and found that the evolution of core technologies in the field of agricultural machinery could be divided into the technology development stage, technology focus stage, and technology transformation stage; this allows us to analyze the evolution and integration of these core technologies. The internal laws of the technology evolution provide a reference for future research plans of governments, enterprises, and institutions, aiding in the patent portfolio planning by revealing the microlevel process of technology integration and the technological trends in agricultural machinery.

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Section: Text Miningmentioning

confidence: 99%

Exploring the Evolution of Core Technologies in Agricultural Machinery: A Patent-Based Semantic Mining Analysis

Wei,

Jiang,

Feng

et al. 2023

Electronics

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“…Although in the field of computer science, network embedding has been widely studied and applied to problems of node classification (e.g., Zuo et al, 2018), link prediction (Jiao et al ., 2021), community detection (e.g., Sun et al, 2020), in the field of scientometrics and informatics, the method has not been fully explored. Some studies have used network embedding methods to identify authors of an anonymized paper (Chen & Sun, 2017; Zhang et al, 2018), measure patent‐level knowledge exploration distance (Choi & Yoon, 2022), recommend academic collaborators (Chen et al, 2021), and visualize collaboration networks and identify core nodes and communities (Zhao et al, 2021). To the best of our knowledge, network embedding has not yet been fully utilized in coauthorship prediction.…”

Section: Related Workmentioning

confidence: 99%

Predicting coauthorship using bibliographic network embedding

Zhu

Quan

Chen

et al. 2022

Asso for Info Science & Tech

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Coauthorship prediction applies predictive analytics to bibliographic data to predict authors who are highly likely to be coauthors. In this study, we propose an approach for coauthorship prediction based on bibliographic network embedding through a graph‐based bibliographic data model that can be used to model common bibliographic data, including papers, terms, sources, authors, departments, research interests, universities, and countries. A real‐world dataset released by AMiner that includes more than 2 million papers, 8 million citations, and 1.7 million authors were integrated into a large bibliographic network using the proposed bibliographic data model. Translation‐based methods were applied to the entities and relationships to generate their low‐dimensional embeddings while preserving their connectivity information in the original bibliographic network. We applied machine learning algorithms to embeddings that represent the coauthorship relationships of the two authors and achieved high prediction results. The reference model, which is the combination of a network embedding size of 100, the most basic translation‐based method, and a gradient boosting method achieved an F1 score of 0.9 and even higher scores are obtainable with different embedding sizes and more advanced embedding methods. Thus, the strengths of the proposed approach lie in its customizable components under a unified framework.

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Measuring Unequal Knowledge Distance by Network Embedding and Multiple Relationships

Wu,

Kang,

Xie

et al. 2023

Proceedings of the Association for Information Science and Tech

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Knowledge distance, representing the dissimilarity between different knowledge units, has been considered as an important dimension of recombination novelty and technological innovation. Previous measurements merely rely on the citation relationship and ignore their directions and weights. To fill this gap, this study proposes a new measurement which not only captures the unequal citation relationship but also integrates multiple information to depict knowledge distance. The results show that our method can accurately portray the knowledge distance in both scientific areas and technical fields.

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Measuring knowledge exploration distance at the patent level: Application of network embedding and citation analysis

Cited by 18 publications

References 37 publications

Exploring the Evolution of Core Technologies in Agricultural Machinery: A Patent-Based Semantic Mining Analysis

Exploring the Evolution of Core Technologies in Agricultural Machinery: A Patent-Based Semantic Mining Analysis

Predicting coauthorship using bibliographic network embedding

Measuring Unequal Knowledge Distance by Network Embedding and Multiple Relationships

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