Spam Email Classification using an Adaptive Ontology

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