TY - JOUR
T1 - Tracing the evolution of service robotics
T2 - Insights from a topic modeling approach
AU - Savin, Ivan
AU - Ott, Ingrid
AU - Konop, Chris
N1 - Funding Information:
Financial support from the Helmholtz Association (HIRG-0069) is gratefully acknowledged. Ivan Savin acknowledges support from the Russian Science Foundation [RSF grant number 19-18-00262]. This work has benefited from presentations at workshops in Kiel, Strasbourg, Karlsruhe, the EMAEE conference in Brighton and the EAEPE conference in Bilbao. All remaining shortcomings are our responsibility.
Publisher Copyright:
© 2021 The Author(s)
PY - 2022/1/1
Y1 - 2022/1/1
N2 - 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 represented by a topic graph, where some topics appear isolated while others are highly interconnected. Furthermore, utilizing external reference texts that characterize service robots from a technical perspective, we propose and apply a novel approach to match the constructed topics to service robotics. The matching procedure is based on frequency and exclusivity of words overlapping between the patents and the reference texts. 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 as well as to new practices of utilizing the topic modeling approach, matching the resulting topics to external classifications and applying to them metrics from graph theory.
AB - 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 represented by a topic graph, where some topics appear isolated while others are highly interconnected. Furthermore, utilizing external reference texts that characterize service robots from a technical perspective, we propose and apply a novel approach to match the constructed topics to service robotics. The matching procedure is based on frequency and exclusivity of words overlapping between the patents and the reference texts. 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 as well as to new practices of utilizing the topic modeling approach, matching the resulting topics to external classifications and applying to them metrics from graph theory.
KW - Knowledge diffusion
KW - Latent dirichlet allocation
KW - Networks
KW - Patents
KW - Topic matching
UR - http://www.scopus.com/inward/record.url?scp=85118479584&partnerID=8YFLogxK
UR - https://www.mendeley.com/catalogue/4743aedb-6dd2-3351-9a21-8d7b5536e465/
U2 - 10.1016/j.techfore.2021.121280
DO - 10.1016/j.techfore.2021.121280
M3 - Article
AN - SCOPUS:85118479584
VL - 174
JO - Technological Forecasting and Social Change
JF - Technological Forecasting and Social Change
SN - 0040-1625
M1 - 121280
ER -