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Learning to Classify Identity Web References Using RDF Graphs

Research output: Contribution to conference Poster

Publication date2009
Number of pages2
<mark>Original language</mark>English


OtherPoster Track of the International Semantic Web Conference 2009
CountryUnited States
Period20/10/09 → …


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.