RDFグラフの多様性に対する汎用カーネル関数

• 荒井 大地

  電気通信大学大学院情報理工学研究科情報・ネットワーク工学専攻

• 兼岩 憲

  電気通信大学大学院情報理工学研究科情報・ネットワーク工学専攻

書誌事項

タイトル別名

• A Generic Kernel for Various RDF Graphs

抄録

Many kernels for RDF graphs have been designed to apply to machine learning such as classification and clustering. However, the performances of these kernels are affected by the variety of RDF graphs and machine learning problems. For dealing with the lack of robustness, this study proposes a generic kernel function called skip kernel that is a generalized of the existing PRO kernel. We formalize a feature extraction in the skip kernel that replaces some edges and nodes (corresponding to predicates and objects) of each resource with variables in a RDF graph. The skip kernel is effectively computed by (i) a recursive process of constructing each set of resources from RDF graphs and (ii) a size calculation of the intersection of two sets of skip structures for resources. We show that the time and space complexities of computing the skip kernel are reduced from O(d(2MN)d) and O(d(M +1)d-1MN) to O((M +1)d-1MN2) and O(M +dN), respectively. In our experiments, several kernels (skip, hop, PRO, walk, path, full subtree, and partial subtree) with SVMs are applied to ten classification tasks for resources on four RDF graphs. The experiments show that the skip kernel outperforms the other kernels with respect to the accuracy of the classification tasks.

収録刊行物

• 人工知能学会論文誌

  人工知能学会論文誌 33 (5), B-I12_1-14, 2018-09-01

  一般社団法人 人工知能学会

キーワード

• semantic web
• RDF
• machine learning
• kernel
• feature extraction

詳細情報

• CRID: 1390282763039765760
• NII論文ID: 130007481108
• DOI: 10.1527/tjsai.b-i12
• ISSN: 13468030
• ISSN: 13460714
• Web Site: https://www.jstage.jst.go.jp/article/tjsai/33/5/33_B-I12/_pdf
• 本文言語コード: ja
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