| Download | - View final version: Patent data analysis (PDF, 1.7 MiB)
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| DOI | Resolve DOI: https://doi.org/10.4224/40001815 |
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| Author | Search for: Xu, Anbo1; Search for: Wang, Yunli1 |
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| Affiliation | - National Research Council of Canada. Digital Technologies
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| Format | Text, Technical Report |
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| Physical description | 16 p. |
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| Subject | text mining; business intelligence; representation learning |
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| Abstract | This project mainly focused on the patent analysis in the KIND (Knowledge and Innovation Network Data) project, a data repository which links patent data to academic funding data and industrial funding data. In this project, we mainly conducted three sections of work on patent data: topic models and competitor analysis using LDA (Latent Dirichlet Allocation) models, patent classification using GCN (Graph Convolutional Network), and information pathway. The experiments show that LDA is able to identify technology trends in patents and GCN’s performance is great on large patent datasets using citation networks as graphs and BOW (bag of words) vectors as features. GCN performs well with a small portion of training data. We are also able to visualize dynamic information flow through information pathway. |
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| Publication date | 2019-08-28 |
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| Publisher | National Research Council of Canada |
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| Language | English |
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| Peer reviewed | No |
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| Export citation | Export as RIS |
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| Report a correction | Report a correction (opens in a new tab) |
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| Record identifier | a36eefbc-961c-4437-84ef-5578a029a394 |
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| Record created | 2019-12-19 |
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| Record modified | 2022-06-03 |
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