Presented at: 8th International Semantic Web Conference (ISWC2009)http://kcap09.stanford.edu/share/posterDemos/154/index.html
The need to monitor a person's web presence has risen in recent years due to identity theft and lateral surveillance becoming prevalent web actions. In this paper we present a machine learning-inspired bootstrapping approach to monitor identity web references that only requires as input an initial small seed set of data modelled as an RDF graph. We vary the combination of different RDF graph matching paradigms with different machine learning classifiers and observe the effects on the classification of identity web references. We present preliminary results of an evaluation in order to show the variation in accuracy of these different permutations.
Keywords: Semantic Web
Resource URI on the dog food server: http://data.semanticweb.org/conference/iswc/2009/paper/poster_demo/154
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