A text mining-based approach for modelling technical knowledge evolution in patents Online publication date: Mon, 30-Nov-2020
by Geng Li; Zuhua Jiang; Xinyu Li
International Journal of Technology, Policy and Management (IJTPM), Vol. 20, No. 4, 2020
Abstract: This paper proposes a method for portraying the evolution of patent technical knowledge (PTK) and provides enterprises with an understanding of the evolution of a specific field. The paper proposes a text mining-based knowledge evolution modelling method. Based on weighted factors, PTKs are obtained from the bibliographic title, abstract and claims (TAC) data on granted patents. Then, PTK clusters are captured by affinity propagation (AP) and expressed with the PTK topics extracted via latent dirichlet allocation (LDA). Furthermore, an association measurement for PTK is proposed, and a long-term evolutionary process for a specific field is revealed by an alluvial diagram. A case study of the new energy vehicles (NEV) industry is presented, and the results indicate that the proposed method can be used to automatically obtain technical knowledge from patent documents and to better measure and analyse the evolution of PTK relative to traditional evolution methods.
Existing subscribers:
Go to Inderscience Online Journals to access the Full Text of this article.
If you are not a subscriber and you just want to read the full contents of this article, buy online access here.Complimentary Subscribers, Editors or Members of the Editorial Board of the International Journal of Technology, Policy and Management (IJTPM):
Login with your Inderscience username and password:
Want to subscribe?
A subscription gives you complete access to all articles in the current issue, as well as to all articles in the previous three years (where applicable). See our Orders page to subscribe.
If you still need assistance, please email subs@inderscience.com
Our Blog 
Follow us on Twitter 
Visit us on Facebook