Please use this identifier to cite or link to this item: http://hdl.handle.net/10419/231569
Authors: 
Ott, Ingrid
Savin, Ivan
Konop, Chris
Year of Publication: 
2021
Series/Report no.: 
Kiel Working Paper No. 2180
Abstract: 
Taking robotic patents between 1977 and 2017 and building upon the topic modeling technique, we extract their latent topics, analyze how important these topics are over time, and how they are related to each other looking at how often they are recombined in the same patents. This allows us to differentiate between more and less important technological trends in robotics based on their stage of diffusion and position in the space of knowledge, where some topics appear isolated while others are highly interconnected. Furthermore, we propose a novel approach to match the constructed topics to the IFR classification of service robots based on frequency and exclusivity of words overlapping between them. We identify around 20 topics belonging to service robotics. Our results corroborate earlier findings, but also provide novel insights on the content and stage of development of application areas in service robotics. With this study we contribute to a better understanding of the highly dynamic field of robotics and contribute to new practices of utilizing the topic modeling approach.
Subjects: 
knowledge diffusion
latent Dirichlet allocation
networks
patents
topic matching
JEL: 
C11
C15
O33
O34
Document Type: 
Working Paper

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