Fuzzy conceptual knowledge processing

We introduce fuzziness to conceptual knowledge processing by using linguistic variables instead of a two-valued representation. The attribute/object table for conceptual lattices holds fuzzy membership values rather than TRUE/FALSE entries and can be mapped into a graph of dependencies. From this graph implications can be extracted together with the method to compute truth values for the inferible conclusions. Hence, fuzzy conclusions can be drawn from interpreting the fuzzy concept values of the graph. We demonstrate the feasibility of our approach by computing patient data from a medical diagnosis example.