A Fast and Simple Graph Kernel for RDF
| Authors | |
|---|---|
| Publication date | 2013 |
| Host editors |
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| Book title | Proceedings of the International Workshop on Data Mining on Linked Data, with Linked Data Mining Challenge |
| Book subtitle | collocated with the European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECMLPKDD 2013) : Prague, Czech Republic, September 23, 2013 |
| Series | CEUR Workshop Proceedings |
| Event | DMoLD 2013: Data Mining on Linked Data with Linked Data Mining Challenge |
| Number of pages | 12 |
| Publisher | Aachen: CEUR-WS |
| Organisations |
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| Abstract |
In this paper we study a graph kernel for RDF based on constructing a tree for each instance and counting the number of paths in that tree. In our experiments this kernel shows comparable classification performance to the previously introduced intersection subtree kernel, but is significantly faster in terms of computation time. Prediction performance is worse than the state-of-the-art Weisfeiler Lehman RDF kernel, but our kernel is a factor 10 faster to compute. Thus, we
consider this kernel a very suitable baseline for learning from RDF data. Furthermore, we extend this kernel to handle RDF literals as bag-ofwords feature vectors, which increases performance in two of the four experiments. |
| Document type | Conference contribution |
| Language | English |
| Published at | http://ceur-ws.org/Vol-1082/paper2.pdf |
| Other links | http://ceur-ws.org/Vol-1082/ |
| Downloads |
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