Combined graph kernels for automatic patent classification: A hybrid approach. (June 2019)

• Record Type: Journal Article
• Authors: Nugroho, Budi; Aritsugi, Masayoshi; Otachi, Yota; Manabe, Yuki
• Is Part Of: World patent information. Volume 57(2019)
• Page Start: 18
• Page End: 24
• Publication Date: 2019-06
• Subjects: Patent citation graph -- Graph kernels -- Combined kernels -- Automatic patent classification; Patent literature -- Periodicals; Information storage and retrieval systems -- Patent documentation -- Periodicals; 608.05
• DOI: 10.1016/j.wpi.2019.03.002
• Languages: English
• ISSNs: 0172-2190

Abstract

Abstract: In this study, we proposed combined kernel-based methods to leverage patent citation graph performance for patent classification. The concept is to use the combined graph kernels of the citation graph to classify patent documents, as a hybrid approach. A multiple kernel framework was used for integrating multiple datasets of various kernels into a combined kernel. We employed seven graph kernels as the baselines and the combination of random walks and Weisfeiler–Lehman subtree kernels to achieve higher performance. We calculated the kernel values of each patent pairwise and employed an SVM classifier to carry out the classification task. The investigation results demonstrate that the combined graph kernel outperforms single kernels.