Structure clustering for Chinese patent documents

Authors:
Su-Hsien Huang;Hao-Ren Ke;Wei-Pang Yang
Affiliations:
Institute of Computer Science and Engineering, National Chiao Tung University, 1001 Ta Hsueh Road, Hsinchu, Taiwan, ROC and Department of Information Management, Minghsin University of Science and ...;Library and Institute of Information Management, National Chiao Tung University, 1001 Ta Hsueh Road, Hsinchu, Taiwan, ROC;Department of Information Management, National Don Hwa University, 1, Section 2, Da Hsueh Road, Shou-Feng, Hualien, Taiwan, ROC
Venue:
Expert Systems with Applications: An International Journal
Year:
2008

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Abstract

This paper aims to cluster Chinese patent documents with the structures. Both the explicit and implicit structures are analyzed to represent by the proposed structure expression. Accordingly, an unsupervised clustering algorithm called structured self-organizing map (SOM) is adopted to cluster Chinese patent documents with both similar content and structure. Structured SOM clusters the similar content of each sub-part structure, and then propagates the similarity to upper level ones. Experimental result showed the maps size and number of patents are proportional to the computing time, which implies the width and depth of structure affects the performance of structured SOM. Structured clustering of patents is helpful in many applications. In the lawsuit of copyright, companies are easy to find claim conflict in the existent patents to contradict the accusation. Moreover, decision-maker of a company can be advised to avoid hot-spot aspects of patents, which can save a lot of R&D effort.