Robots need to have knowledge of their environment to be able to successfully complete service tasks. Most knowledge inference mechanisms assume complete and correct knowledge about the environment. Real world environments are often uncertain and only partially observable. Thus, intelligent service robots may have an incomplete knowledge base which includes true positives as well as false negatives and false positives. False negatives and false positives can prevent service robots from completing their service tasks. In the field of logical inference, false positives are a more significant problem compared to false negatives. A weighted ontology and association mechanism was proposed in previous research which recommended improvisational goal-oriented actions that could be applied in the case of false negatives. However, false positives are not usually matched in the existing ontological semantic network. Consequently, the association mechanism does not work. To deal with false positives, the weighted ontology and association mechanism were extended by adding additional nodes which are associated with epistemic actions. The proposed method was successfully evaluated and verified through experiments; results show that almost all problems associated with false positives and false negatives were resolved.