Data Models for Dealing with Linguistic and Imprecise Information
Guoqing Chen · Studies in fuzziness and soft computing · 1999
Data models play an important role in dealing with linguistic and imprecise information. This paper first describes fuzzy extensions to ER/EER concepts so that uncertainty and imprecision in data and semantics can be dealt with at a conceptual level. Fuzzy extensions to the concepts such as superclass/subclass, generalization/specialization, and shared subclass/category are discussed. The attribute inheritance is investigated in a fuzzy context, including multiple inheritance, selective inheritance, and the inheritance for derived attributes. Furthermore, certain constraints on relationships are explored in terms of the inheritance constraint, the participation constraint, and the cardinality constraint. At the (ordinary) data level, imprecision and uncertainty inherent in attribute values, database queries and integrity constraints are dealt with in fuzzy relational database models. The issues concerned center around fuzzy data representation and storage, data manipulation and extended algebraic operators, update anomalies, and information maintenance. These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.