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Discovering research areas from patents: A case study in autonomous vehicles industry
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Publication Year
2021-01-01
Journal
Proceedings - 2021 IEEE International Conference on Big Data and Smart Computing, BigComp 2021
Publisher
Institute of Electrical and Electronics Engineers Inc.
Citation
Proceedings - 2021 IEEE International Conference on Big Data and Smart Computing, BigComp 2021, pp.203-209
Keyword
Autonomous VehiclesBig DataNatural Language ProcessingPatents AnalysisTopic Modelling
Mesh Keyword
Autonomous systemsBaseline modelsKeyword-clusteringLatent dirichlet allocationsLatent Semantic AnalysisUS patents
All Science Classification Codes (ASJC)
Artificial IntelligenceComputer Science ApplicationsComputer Vision and Pattern RecognitionInformation SystemsSignal ProcessingInformation Systems and Management
Abstract
Seven research areas introduced by the 'Autonomous Systems' research lab provide research areas required to enable the autonomous vehicle industry. For ensuring the validity of the research areas with the baseline, we apply Latent Semantic Analysis (LSA) and Latent Dirichlet Allocation (LDA) on the US patents containing 'Autonomous Vehicles' to identify keywords and research areas of relevant technologies. Keyword clustering and TF-IDF are repeatedly applied to the retrieved keywords to further filter out irrelevant words. Coherence values for LSA and LDA are evaluated to determine an adequate number of topics that need to be generated. We found that results from LSA provide a list of technologies already included in the baseline while topics from LDA provide associated keywords to support defining each technology. We conclude the numbers and topics provided by the baseline model closely represent the industry of autonomous vehicles but the identified topics from us provide a significant extension in research areas. The resulting research areas may provide overviews and guidelines on the autonomous vehicles industry for researchers and institutes
Language
eng
URI
https://aurora.ajou.ac.kr/handle/2018.oak/36672
https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85102976823&origin=inward
DOI
https://doi.org/10.1109/bigcomp51126.2021.00046
Journal URL
http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=9373068
Type
Conference
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Joo, Yeoun.Lee이주연
Department of Industrial Engineering
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