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Making data-querying and representation easier and more human consistent is an important research topic. In this context, fuzzy logic with its capability to model linguistic expressions provides an interesting framework, which has been adopted by many researchers. However, there are still some aspects that have not been adequately covered. In particular, it becomes widely advocated that while communicating, humans give both positive and negative information to state what they desire and what they reject. Because positive and negative statements do not necessarily mirror each other, this results in so-called heterogeneous bipolar information. Traditional fuzzy approaches do not adequately support the handling of heterogeneous bipolar information in information systems. Therefore, there is a need for more advanced techniques. In this paper, how bipolarity can be dealt with in the formulation and evaluation of selection conditions in fuzzy querying within a possibilistic, relational database framework is presented. Three novel query-evaluation techniques based on interval-valued fuzzy sets, Atanassov fuzzy sets, and twofold fuzzy sets are presented and compared with each other. Possibility theory is used to deal with uncertainty. Special attention is paid to the description of the semantics, use, benefits, and drawbacks of each formalism.