A Scalable Kernel Approach to Learning in Semantic Graphs with Applications to Linked Data
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چکیده
In this paper we discuss a kernel approach to learning in semantic graphs. To scale up the performance to large data sets, we employ the Nyström approximation. We derive a kernel derived from semantic relations in a local neighborhood of a node. One can apply our approach to problems in multi-relational domains with several thousand graph nodes and more than a million potential links. We apply the approach to DBpedia data extracted from the RDF-graph of the Semantic Web’s Linked Open Data (LOD).
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تاریخ انتشار 2010