Knowledge reduction is an important issue in data mining. This paper focuses on the problem of knowledge reduction in incomplete decision tables. Based on a concept of incomplete conditional entropy, a new reduct definition is presented for incomplete decision tables and its properties are analyzed. Compared with several existing reduct definitions, the new definition has a better explanation for knowledge uncertainty and is more convenient for application of the idea of approximate reduct in incomplete decision tables.