The Modeling and Analyzing Methods of Weighted Knowledge Network for Domain Knowledge Based on Keywords Clustering

Xiao, Liao; Li, Zhihong; Xi, Yunjiang Xi

doi:10.2174/1874110x01408010990

Cited by 2 publications

(2 citation statements)

References 17 publications

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“…The content network breaks the constraint of classifying the same thinking activity based on time series only, and its formation process is mainly based on the results of cluster analysis(Liao, 2016) [7 ]. The clustering of content nodes of the same type together to form a cluster of nodes is known as clustering.…”

Section: Content Network Analysismentioning

confidence: 99%

A Quantitative Modeling Analysis Method of Mental Models Combined with Link Tables

YUAN,

LUO

2023

Proceedings of the 2nd International Conference on Information Economy, Data Modeling and Cloud Computing, ICIDC 2023, June 2–4

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As the basic structure of human cognition, mental models manifest the structure of people's cognition, experience and knowledge of the real world. Most of the existing studies focus on the structure and content of mental models, and a more objective and comprehensive approach to modeling mental models has not been proposed. To address this issue, firstly, the unstructured text data is transformed into a structured link matrix from link relationship representation for further computational work. A page ranking algorithm is also introduced to identify the important nodes of the mental model network to achieve key intent capture and design concept transformation. After that, a content network-based clustering analysis is used to identify the evolutionary process of users' mental activities. Finally, a case study analysis is utilized to verify the effectiveness of this modeling and analysis method, which provides a reference for future theoretical research and application practice in related fields.

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Section: Content Network Analysismentioning

confidence: 99%

A Quantitative Modeling Analysis Method of Mental Models Combined with Link Tables

YUAN,

LUO

2023

Proceedings of the 2nd International Conference on Information Economy, Data Modeling and Cloud Computing, ICIDC 2023, June 2–4

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“…However, selecting the relevance threshold to distinguish between lead users and regular users is not yet clear. Based on Liao's research results [54], we considered that the operators might determine the threshold by two methods: 1. The total amount (S) of valid information in the content contributed by the user can be used as the threshold value.…”

Section: Conclusion and Limitationsmentioning

confidence: 99%

How to Find the Key Participants in Crowdsourcing Design? Identifying Lead Users in the Online Context Using User-Contributed Content and Online Behavior Analysis

et al. 2022

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Lead users are the most valuable innovation sources in crowdsourcing design; how to identify these users is a research hotspot in the field of design and management. Existing approaches to discover lead users in the context of the online community, such as the manual method and ordering algorithm, have some limitations, for instance, low coverage and accuracy. To address these deficiencies, this article proposes a method that applies text-mining techniques, analysis of user behavior, and contributed content to identify lead users. We suggest a three-step analytical approach: First, a criterion system to evaluate the user’s leading-edge status is constructed. Second, we utilize a fuzzy analytical hierarchy process to assess the weighted value of each indicator and develop the reference sequence of the indicators. Third, grey relational analysis is employed to analyze the correlations between users’ indicators and reference sequences, and lead users are recognized based on each user’s correlation ranking. An empirical analysis is used to examine the effectiveness of the proposed method. The results reveal that the method has good precision and recall rate, can automatically process large-scale data, and has no strict requirements for respondents. Finally, the article discusses the limitations and provides possible directions for future research.

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The Modeling and Analyzing Methods of Weighted Knowledge Network for Domain Knowledge Based on Keywords Clustering

Cited by 2 publications

References 17 publications

A Quantitative Modeling Analysis Method of Mental Models Combined with Link Tables

A Quantitative Modeling Analysis Method of Mental Models Combined with Link Tables

How to Find the Key Participants in Crowdsourcing Design? Identifying Lead Users in the Online Context Using User-Contributed Content and Online Behavior Analysis

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