Unauthorized service or product advertising messages sent via electronic mails are called as spam e-mails. Detecting spam e-mail remains a challenging task. Existing countermeasures based on the statistical keyword, conceptual and IP address-based blacklists are not efficient due to difficulty in finding new attack patterns generated by the Internet of Things botnet devices. The other spam detection approaches rely on a hybrid of conceptual knowledge engineering with machine learning techniques. But, modern spammers evade the hybrid techniques through word polysemy and word ambiguity due to the context-sensitive nature of words. In this paper, the integration of Naïve Bayesian classification with conceptual and semantic similarity technique is proposed to combat the ambiguity raised through polysemy in spam detection. To analyse the effectiveness of our approach, the experiments were conducted on benchmark data sets such as Spambase, PU1, Enron corpus, and Ling-spam. From the experimental results, it is evident that our proposed system achieves high accuracy of 98.89% than the existing approaches. © 2019, Springer Science+Business Media, LLC, part of Springer Nature.