JOURNAL OF SOFTWARE (JSW)
ISSN : 1796-217X
Volume : 2    Issue : 3    Date : September 2007

Spam Email Classification using an Adaptive Ontology
Seongwook Youn and Dennis McLeod
Page(s): 43-55
Full Text:
PDF (858 KB)


Abstract
Email has become one of the fastest and most economical forms of communication. However, the
increase of email users has resulted in the dramatic increase of spam emails during the past few
years. As spammers always try to find a way to evade existing filters, new filters need to be
developed to catch spam. Ontologies allow for machine-understandable semantics of data. It is
important to share information with each other for more effective spam filtering. Thus, it is necessary
to build ontology and a framework for efficient email filtering. Using ontology that is specially
designed to filter spam, bunch of unsolicited bulk email could be filtered out on the system. Similar
to other filters, the ontology evolves with the user requests. Hence the ontology would be
customized for the user. This paper proposes to find an efficient spam email filtering method using
adaptive ontology

Index Terms
spam filter, ontology, data mining, text classification, feature extraction