Semantic spam filtering from personalized ontologies

One of the biggest problems that Internet faces is the increase of email spam. The main drawback with previous anti-spam filters is that they are based only on 1) the syntactical features of words lacking semantic analysis, or 2) on what the majority of users regard as spam without considering the individual preferences of a particular user. In this paper we present a spam email filter that personalizes its filtering process using an email user profile that contains the user's preferences regarding emails. Our innovative email user profile is based not only on some common user profiling techniques but also on the knowledge contained in a domain ontology. The user profile is used to learn which spam emails (although unsolicited and large-scale sent) are interesting for the user, despite they are spam. The encouraging experimental results provide empirical evidence of the effectiveness of using an ontological approach to user profiling in an email spam filter.

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