A Fast Approximation of the Weisfeiler-Lehman Graph Kernel for RDF Data
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| Publication date | 2013 |
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| Book title | Machine Learning and Knowledge Discovery in Databases |
| Book subtitle | European Conference, ECML PKDD 2013, Prague, Czech Republic, September 23-27, 2013: proceedings |
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| Series | Lecture Notes in Computer Science |
| Event | Machine learning and knowledge discovery in databases: European Conference, ECML PKDD 2013 |
| Volume | Issue number | 1 |
| Pages (from-to) | 606-621 |
| Publisher | Heidelberg: Springer |
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| Abstract |
In this paper we introduce an approximation of the Weisfeiler-Lehman graph kernel algorithm aimed at improving the computation time of the kernel when applied to Resource Description Framework (RDF) data. Typically, applying graph kernels to RDF is done by extracting subgraphs from a large RDF graph and computing the kernel on this set of subgraphs. In contrast, our algorithm computes the Weisfeiler-Lehman kernel directly on the large RDF graph, but still retains the subgraph information. We show that this algorithm is faster than the regular Weisfeiler-Lehman kernel for RDF data and has at least the same performance. Furthermore, we show that our method has similar or better performance, and is faster, than other recently introduced graph kernels for RDF.
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| Document type | Conference contribution |
| Language | English |
| Published at | https://doi.org/10.1007/978-3-642-40988-2_39 |
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