Decision Support Systems based on Answer Set Programing

Description logics is a group of logics for knowledge modeling, which are derived from semantic networks. They are to be understood essentially as accessible fragments of predicate logic of first order, to allow strong expressions to be expressed through a complexity class with restrictions. Other description logics provide such strong expressions in polynomial complexity. For description logics, there are a special inputs and intuitive notation that facilitates the handling of them substantially. Modeling in expert systems is very important, especially systems are having a complex domain where the spatial and temporal data are needed to be modeled. The ontology model permits the use of a reasoner that can check definitions of the statements in the ontology, if they are consistent or not. It can also recognize which concepts are the best for which definitions. The reasoner can therefore help to maintain the hierarchy correctly. This is particularly helpful when dealing with multiple classes hierarchies, where for an expert system complex concepts can therefore be built up in definitions out of simpler concepts. So, An ontology is an engineering artefact or is a formal representation of knowledge as a set of concepts within a domain, it often includes classification based information and constraints capturing the knowledge about the domain [13]. Despite of the powerful semantic of ontology languages, they lead to high querying execution time and cause a not real time inferring on the defined ontology. In this lecture, we show the efficiency of Answer Set Programing (ASP) to represent our knowledge database and then how to define the rules between all individuals of the desired ontology. Finally, we compare the execution time of an ASP-based reasoner and other existing reasoning paradigms.