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Substructure Counting Graph Kernels for Machine Learning from RDF Data

36 Pages Posted: 17 Jan 2020 First Look: Accepted

See all articles by Gerben Klaas Dirk de Vries

Gerben Klaas Dirk de Vries

University of Amsterdam - System and Network Engineering Group

Steven de Rooij

University of Amsterdam - System and Network Engineering Group

Abstract

In this paper we introduce a framework for learning from RDF data using graph kernels that count substructures in RDF graphs, which systematically covers most of the existing kernels previously defined and provides a number of new variants. Our definitions include fast kernel variants that are computed directly on the RDF graph. To improve the performance of these kernels we detail two strategies. The first strategy involves ignoring the vertex labels that have a low frequency among the instances. Our second strategy is to remove hubs to simplify the RDF graphs. We test our kernels in a number of classification experiments with real-world RDF datasets. Overall the kernels that count subtrees show the best performance. However, they are closely followed by simple bag of labels baseline kernels. The direct kernels substantially decrease computation time, while keeping performance the same. For the walks counting kernel the decrease in computation time of the approximation is so large that it thereby becomes a computationally viable kernel to use. Ignoring low frequency labels improves the performance for all datasets. The hub removal algorithm increases performance on two out of three of our smaller datasets, but has little impact when used on our larger datasets.

Keywords: Graph Kernels, Machine Learning for RDF, Weisfeiler-Lehman, Hub Removal

Suggested Citation

Klaas Dirk de Vries, Gerben and de Rooij, Steven, Substructure Counting Graph Kernels for Machine Learning from RDF Data (2015). Journal of Web Semantics First Look. Available at SSRN: https://ssrn.com/abstract=3198919 or http://dx.doi.org/10.2139/ssrn.3198919

Gerben Klaas Dirk de Vries (Contact Author)

University of Amsterdam - System and Network Engineering Group ( email )

Spui 21
Amsterdam, 1018 WB
Netherlands

Steven De Rooij

University of Amsterdam - System and Network Engineering Group ( email )

Spui 21
Amsterdam, 1018 WB
Netherlands

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