Integrating Incomplete Information into the Relational Data Model

Knowledge and belief are generally incomplete, contradictory, or even error sensitive, being desirable to use formal tools to deal with the problems that arise from the use of partial, contradictory, ambiguous, imperfect, nebulous, or missing information. Historically, uncertain reasoning has been associated with probability theory. However, qualitative models and qualitative reasoning have been around in database theory and Artificial Intelligence research for some time, in particular due to the growing need to offer user support in decision making processes. In this paper, and under the umbrella of the Multi-valued Extended Logic Programming formalism to knowledge representation and reasoning we present an evaluative perspective of such an approach, in order to select the best theories (or logic programs) that model the universe of discourse to solve a problem, in terms of a process of quantification of the quality-of-information that stems out from those theories. Additionally, we present a novel approach to integrate incomplete information into the relational data model, making possible the use of the relational algebra operators and the potential inherent to the Structured Query Languages to present solutions to a particular problem and to measure their degree of self-reliance.