A Generic Kernel for Various RDF Graphs
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- Arai Daichi
- Department of Computer and Network Engineering, Graduate School of Informatics and Engineering, The University of Electro-Communications
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- Kaneiwa Ken
- Department of Computer and Network Engineering, Graduate School of Informatics and Engineering, The University of Electro-Communications
Bibliographic Information
- Other Title
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- RDFグラフの多様性に対する汎用カーネル関数
Abstract
<p>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.</p>
Journal
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- Transactions of the Japanese Society for Artificial Intelligence
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Transactions of the Japanese Society for Artificial Intelligence 33 (5), B-I12_1-14, 2018-09-01
The Japanese Society for Artificial Intelligence
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Details 詳細情報について
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- CRID
- 1390282763039765760
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- NII Article ID
- 130007481108
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- ISSN
- 13468030
- 13460714
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- Text Lang
- ja
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- Data Source
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- JaLC
- Crossref
- CiNii Articles
- KAKEN
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- Abstract License Flag
- Disallowed