Abstract
Classifying and organizing documents in repositories is an active research topic in digital library studies. Manually classifying the large volume of patents and patent applications managed by patent offices is a labor-intensive task. Many previous studies have employed patent contents for patent classification with the aim of automating this process. In this research we propose to use patent citation information, especially the citation network structure information, to address the patent classification problem. We adopt a kernel-based approach and design kernel functions to capture content information and various citation-related information in patents. These kernels. performances are evaluated on a testbed of patents related to nanotechnology. Evaluation results show that our proposed labeled citation graph kernel, which utilized citation network structures, significantly outperforms the kernels that use no citation information or only direct citation information.
| Original language | English (US) |
|---|---|
| Title of host publication | Proceedings of the ACM International Conference on Digital Libraries |
| Pages | 419-427 |
| Number of pages | 9 |
| DOIs | |
| State | Published - 2007 |
| Event | 7th ACM/IEEE Joint Conference on Digital Libraries, JCDL 2007: Building and Sustaining the Digital Environment - Vancouver, BC, Canada Duration: Jun 18 2007 → Jun 23 2007 |
Other
| Other | 7th ACM/IEEE Joint Conference on Digital Libraries, JCDL 2007: Building and Sustaining the Digital Environment |
|---|---|
| Country | Canada |
| City | Vancouver, BC |
| Period | 6/18/07 → 6/23/07 |
Fingerprint
Keywords
- Citation network
- Graph kernel
- Kernel-based method
- Machine learning
- Nanotechnology
- Patent classification
ASJC Scopus subject areas
- Computer Science(all)
- Social Sciences(all)
Cite this
Automatic patent classification using citation network information : An experimental study in nanotechnology. / Li, Xin; Chen, Hsinchun; Zhang, Zhu; Li, Jiexun.
Proceedings of the ACM International Conference on Digital Libraries. 2007. p. 419-427.Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
}
TY - GEN
T1 - Automatic patent classification using citation network information
T2 - An experimental study in nanotechnology
AU - Li, Xin
AU - Chen, Hsinchun
AU - Zhang, Zhu
AU - Li, Jiexun
PY - 2007
Y1 - 2007
N2 - Classifying and organizing documents in repositories is an active research topic in digital library studies. Manually classifying the large volume of patents and patent applications managed by patent offices is a labor-intensive task. Many previous studies have employed patent contents for patent classification with the aim of automating this process. In this research we propose to use patent citation information, especially the citation network structure information, to address the patent classification problem. We adopt a kernel-based approach and design kernel functions to capture content information and various citation-related information in patents. These kernels. performances are evaluated on a testbed of patents related to nanotechnology. Evaluation results show that our proposed labeled citation graph kernel, which utilized citation network structures, significantly outperforms the kernels that use no citation information or only direct citation information.
AB - Classifying and organizing documents in repositories is an active research topic in digital library studies. Manually classifying the large volume of patents and patent applications managed by patent offices is a labor-intensive task. Many previous studies have employed patent contents for patent classification with the aim of automating this process. In this research we propose to use patent citation information, especially the citation network structure information, to address the patent classification problem. We adopt a kernel-based approach and design kernel functions to capture content information and various citation-related information in patents. These kernels. performances are evaluated on a testbed of patents related to nanotechnology. Evaluation results show that our proposed labeled citation graph kernel, which utilized citation network structures, significantly outperforms the kernels that use no citation information or only direct citation information.
KW - Citation network
KW - Graph kernel
KW - Kernel-based method
KW - Machine learning
KW - Nanotechnology
KW - Patent classification
UR - http://www.scopus.com/inward/record.url?scp=36348987776&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=36348987776&partnerID=8YFLogxK
U2 - 10.1145/1255175.1255262
DO - 10.1145/1255175.1255262
M3 - Conference contribution
AN - SCOPUS:36348987776
SN - 1595936440
SN - 9781595936448
SP - 419
EP - 427
BT - Proceedings of the ACM International Conference on Digital Libraries
ER -