Semantic decision table
A semantic decision table uses modern ontology engineering technologies to enhance traditional a decision table. The term "semantic decision table" was coined by Yan Tang and Prof. Robert Meersman from VUB STARLab in 2006. A semantic decision table is a set of decision tables properly annotated with an ontology. It provides a means to capture and examine decision makers’ concepts, as well as a tool for refining their decision knowledge and facilitating knowledge sharing in a scalable manner.
Background
A decision table is defined as a "tabular method of showing the relationship between a series of conditions and the resultant actions to be executed". Following the de facto international standard, a decision table contains three building blocks: the conditions, the actions, and the rules.A decision condition is constructed with a condition stub and a condition entry. A condition stub is declared as a statement of a condition. A condition entry provides a value assigned to the condition stub. Similarly, an action composes two elements: an action stub and an action entry. One states an action with an action stub. An action entry specifies whether the action is to be performed.
A decision table separates the data from the decision templates. Or rather, a decision table can be a tabular result of its meta-rules.
Traditional decision tables have many advantages compared to other decision support manners, such as if-then-else programming statements, decision trees and Bayesian networks. A traditional decision table is compact and easily understandable. However, it still has several limitations. For instance, a decision table often faces the problems of conceptual ambiguity and conceptual duplication; and it is time consuming to create and maintain large decision tables. Semantic decision tables are an attempt to solve these problems.
Definition
A semantic decision table is modeled based on the framework of Developing Ontology-Grounded Methods and Applications. The separation of an ontology into extremely simple linguistic structures and a layer of lexon constraints used by applications, aiming to achieve a degree of scalability.According to the DOGMA framework, a semantic decision table consists of a layer of the decision binary fact types called semantic decision table lexons and a semantic decision table commitment layer that consists of the constraints and axioms of these fact types.
A lexon l is a quintuple where and represent two concepts in a natural language ; and may make use of lexons. A commitment can contain various constraints, rules and axiomatized binary facts based on needs. It can be modeled in different modeling tools, such as object-role modeling, conceptual graph, and Unified Modeling Language.