Abstract
The judgment that whether an email is spam or non-spam may vary from person to person. Different individuals can have totally different responses to the same email based on their preferences. This paper presents an innovative approach that incorporates user preferences to construct an anti-spam mail system, which is different from the conventional content-based approaches. We build a user preference ontology to formally represent the important concepts and rules derived from a data mining process. Then we use an inference engine that utilizes the knowledge to predict the user’s action on new incoming emails. We also suggest a new rule optimization procedure inspired from logic synthesis to improve comprehensibility and exclude redundant rules. Experimental results showed that our user preference based architecture achieved good performance and the rules derived from the architecture and the optimization method have better quality in terms of comprehensibility.
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References
Cormack, G.V.: Overview of the TREC 2005 Spam Track (2005), http://plg.uwaterloo.ca/~gvcormac/trecspamtrack05
Wolfe, P., Scott, C., Erwin, M.: Anti-Spam Tool Kit. McGraw-Hill, New York (2004)
Gray, A., Haahr, M.: Personalized, Collaborative Spam Filtering. In: Proc. of the First Conference on Email and Anti-Spam (2004)
Ravi, J., Shi, W., Xu, C.: Personalized Email Management at Network Edges. IEEE Internet Computing 9(2), 54–60 (2005)
Anti-Spam Firewall, http://www.barracudanetworks.com/ns/products/anti_spam_tech.php
Maedche, A.: Ontology Learning for the Semantic Web. The Kluwer International Series in Engineering and Computer Science, vol. 665. Kluwer Academic Publishers, Dordrecht (2003)
Files, C.M., Perkowski, M.A.: Multi-Valued Functional Decomposition as a Machine Learning Method. In: Proc. of ISMVL ’98, pp. 173–178 (1998)
Chan, A., Freitas, A.: A New Classification-Rule Pruning Procedure for an Ant Colony Algorithm. In: Talbi, E.-G., Liardet, P., Collet, P., Lutton, E., Schoenauer, M. (eds.) EA 2005. LNCS, vol. 3871, pp. 25–36. Springer, Heidelberg (2006)
Sasao, T.: Switching Theory for Logic Synthesis. Kluwer Academic Publishers, Dordrecht (1999)
Kim, J., Kang, S.: Feature Selection by Fuzzy Inference and Its Application to Spam-Mail Filtering. In: Hao, Y., Liu, J., Wang, Y.-P., Cheung, Y.-m., Yin, H., Jiao, L., Ma, J., Jiao, Y.-C. (eds.) CIS 2005. LNCS (LNAI), vol. 3801, pp. 361–366. Springer, Heidelberg (2005)
Witten, I.H., Frank, E.: Data Mining: practical machine learning tools and techniques, 2nd edn. Morgan Kaufmann, San Francisco (2005)
Gruber, T.R.: Toward Principles for the Design of Ontologies Used for Knowledge Sharing. Int. Journal of Human-Computer Studies 43, 907–928 (1995)
McDermott, D., Dou, D.: Representing disjunction and quantifiers in RDF. In: Horrocks, I., Hendler, J. (eds.) ISWC 2002. LNCS, vol. 2342, pp. 250–263. Springer, Heidelberg (2002)
OWL Web Ontology Language, http://www.w3.org/TR/owl-ref/
SWRL: A Semantic Web Rule Language Combining OWL and RuleML, http://www.w3.org/Submission/SWRL/
Dou, D., McDermott, V., Qi, P.: Ontology translation on the semantic web. Journal of Data Semantics 2, 35–57 (2004)
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Kim, J., Dou, D., Liu, H., Kwak, D. (2007). Constructing a User Preference Ontology for Anti-spam Mail Systems. In: Kobti, Z., Wu, D. (eds) Advances in Artificial Intelligence. Canadian AI 2007. Lecture Notes in Computer Science(), vol 4509. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-72665-4_24
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DOI: https://doi.org/10.1007/978-3-540-72665-4_24
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