Paraconsistent Intuitionistic Fuzzy Relational Data Model
  In this paper, we present a generalization of the relational data model based on paraconsistent intuitionistic fuzzy sets. Our data model is capable of manipulating incomplete as well as inconsistent information. Fuzzy relation or intuitionistic fuzzy relation can only handle incomplete information. Associated with each relation are two membership functions one is called truth-membership function $T$ which keeps track of the extent to which we believe the tuple is in the relation, another is called false-membership function which keeps track of the extent to which we believe that it is not in the relation. A paraconsistent intuitionistic fuzzy relation is inconsistent if there exists one tuple $a$ such that $T(a) + F(a) > 1$. In order to handle inconsistent situation, we propose an operator called split to transform inconsistent paraconsistent intuitionistic fuzzy relations into pseudo-consistent paraconsistent intuitionistic fuzzy relations and do the set-theoretic and relation-theoretic operations on them and finally use another operator called combine to transform the result back to paraconsistent intuitionistic fuzzy relation. For this model, we define algebraic operators that are generalisations of the usual operators such as union, selection, join on fuzzy relations. Our data model can underlie any database and knowledge-base management system that deals with incomplete and inconsistent information. 
Rajshekhar Sunderraman, Haibin Wang