The term "semantic decision table" was coined by Yan Tang and Prof. Robert Meersman from VUB STARLab (Free University of Brussels) in 2006.
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.
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".
[2] Following the de facto international standard (CSA, 1970), a decision table contains three building blocks: the conditions, the actions (or decisions), and the rules.
A decision table separates the data (that is the condition entries and decision/action entries) from the decision templates (that are the condition stubs, decision/action stubs, and the relations between them).
Or rather, a decision table can be a tabular result of its meta-rules.
A traditional decision table is compact and easily understandable.
For instance, a decision table often faces the problems of conceptual ambiguity and conceptual duplication[citation needed]; and it is time consuming to create and maintain large decision tables[citation needed].
Semantic decision tables are an attempt to solve these problems.
A semantic decision table is modeled based on the framework of Developing Ontology-Grounded Methods and Applications (DOGMA[3]).
The separation of an ontology into extremely simple linguistic structures (also known as lexons) and a layer of lexon constraints used by applications (also known as ontological commitments), 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.
represent two concepts in a natural language (e.g., English);
– refer to the relationships that the concepts share with respect to one another;
The ontological commitment layer formally defines selected rules and constraints by which an application (or "agent") may make use of lexons.
A commitment can contain various constraints, rules and axiomatized binary facts based on needs.