In this paper we present a semantic-based data mining approach to identify candidate viruses as potential bio-terrorism weapons from biomedical literature. We first identify all the possible properties of viruses as search key words based on Geissler’s 13 criteria; the identified properties are then defined using MeSH terms. Then, we assign each property an importance weight based on domain experts’ judgment. After generating all the possible valid combinations of the properties, we search the biomedical literature, retrieving all the relevant documents. Next our method extracts virus names from the downloaded documents for each search keyword and identifies the novel connection of the virus according to these 4 properties. If a virus is found in the different document sets obtained by several search keywords, the virus should be considered as suspicious and treated as candidate viruses for bio-terrorism. Our findings are intended as a guide to the virus literature to support further studies that might then lead to appropriate defense and public health measures.