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making process by providing automated constraint dependency checking, explanation and
corrective recommendation, and propagation and delete-cascade.
This paper is structured as follows: Literature is surveyed in section two. Knowledge
representation of DSS using variability is demonstrated in section three. Knowledge
validation is illustrated in section four and implementation is discussed in section five.
Section six contains the conclusion and future work.
2. Related work
The aim of knowledge representation is to facilitate effective knowledge management which
concerns expressive representation and efficiency of reasoning in human [15]. Related works
on this area are summarized as follows:
Haas [10] investigated the feasibility of developing an overarching knowledge
representation for Bureau of Labor Statistics information that captured its semantics,
including concepts, terminology, actions, sources, and other metadata, in a uniformly
applicable way. Haas suggested the (ISO/IEC 11179) standard for metadata, as knowledge
representation techniques. Molina [16] reported the advantages of using knowledge
modeling software tool to help developers build a DSS. Molina describes the development
of DSS system called SAIDA where knowledge is represented as components, which was
designed by Knowledge Structure Manager (KSM). KSM is a knowledge-modeling tool that
includes and extends the paradigm of task method-domain followed by different
knowledge engineering methodologies. KSM provides a library of reusable software
components, called primitives of representation that offer the required freedom to the
developer to select the most convenient representation for each case (rules, frames,
constraints, belief networks, etc.).
Froelich and Wakulicz-Deja [8] investigated problems of representing knowledge for a DSS
in the field of medical diagnosis systems. They suggested in [8] a new model of associational
cognitive maps (ACM). The ability to represent and reason with the structures of causally
dependant concept is the theoretical contribution of the proposed ACM. Antal [1] proposed
the bayesian network as a knowledge representation technique to represent multiple-point-
of views. The proposed technique in [1] serves as a refection of multiple points of view and
surpasses bayesian network both by describing dependency constraint rules and an auto-
explanation mechanism. Lu et al. [13] developed a knowledge-based multi-objective DSS.
The proposal in [13] considers both declarative and procedural knowledge. Declarative
knowledge is a description of facts with information about real-world objects and their
properties. Procedural knowledge encompasses problem-solving strategies, arithmetic and
inferential knowledge. Lu et al. [13] used text, tables and diagrams to represent knowledge.
Brewster and O'Hara [3] prove difficulties of representative skills, distributed knowledge, or
diagrammatic knowledge using ontologies. Pomerol et.al [21] used artificial intelligence
decision tree to represent operational knowledge in DSS. Christiansson [4] proposed
semantic web and temporal databases as knowledge representation techniques for new
generation of knowledge management systems. One of the most sophisticated knowledge
modeling methodologies is Common KADS [24]. Common KADS explains how to model a
knowledge application through structural top-down analysis of the problem domain. The
outcome of modeling process according to Common KADS consists of three layers that are
called contextual model, conceptual model and design model. Common KADS model did
not provide mechanism to define relation between objects or between layers. Padma and
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