Semantic decision table

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".{{cite book|title=Z243.1–1970 for Decision Tables|author=Canadian Standards Association|year=1970}} 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 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 (or decision) composes two elements: an action stub and an action entry. One states an action with an action stub. An action entry specifies whether (or in what order) the action is to be performed.

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.

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{{Citation needed|date=December 2009}}; and it is time consuming to create and maintain large decision tables{{Citation needed|date=December 2009}}. 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{{cite book|title=Ontologies and Databases:More than a Fleeting Resemblance|author=Robert Meersman|year=2001|editor=d'Atri, A. |editor2=Missikoff, M.|publisher=Luiss Publication|series=Proc. of OES/SEO 2001 Rome Workshop}}). 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.

A lexon l is a quintuple where $t\_1$ and $t\_2$ represent two concepts in a natural language (e.g., English); $r\_1$ and $r\_2$ (in, $r\_1$ corresponds to "role and $r\_2$ – refer to the relationships that the concepts share with respect to one another; $\backslash gamma$ is a context identifier refers to a context, which serves to disambiguate the terms $t\_1,t\_2$ into the intended concepts, and in which they become meaningful.

For example, a lexon <γ, driver's license, is issued to, has, driver> explains a fact that “a driver’s license is issued to a driver”, and “a driver has a driver’s license”.

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. It can be modeled in different modeling tools, such as object-role modeling, conceptual graph, and Unified Modeling Language.

A semantic decision table contains richer decision rules than a decision table. During the annotation process, the decision makers need to specify all the implicit rules, including the hidden decision rules and the meta-rules of a set of decision tables. The semantics of these rules is derived from an agreement between the decision makers observing the real-world decision problems. The process of capturing semantics within a community is a process of knowledge acquisition.

• {{cite book |title=Z243.1–1970 for Decision Tables |author=Canadian Standards Association |year=1970}}
• {{cite book |title=Towards building semantic decision table with domain ontologies |author1=Yan Tang |author2=Robert Meersman |name-list-style=amp |year=2007 |isbn=978-988-97311-5-1 |editor=C. Man-chung |editor2=J.N.K. Liu |editor3=R. Cheung |editor4=J. Zhou |pages=14–21 |publisher=ISM Press |series=Proceedings of the International Conference of Information Technology and Management (ICITM2007)}}
• {{cite book |author1=Yan Tang |author2=Robert Meersman |name-list-style=amp |title=Towards Building Semantic Decision Tables with Domain Ontologies |year=2008 |editor1=Man-Chung Chan |editor2=Ronnie Cheung |editor3=James N K Liu |publisher=World Scientific |isbn=978-981-281-906-2 |series=Challenges in Information Technology Management}}
• {{cite book |author=Yan Tang, Robert Meersman and Jan Vanthienen |title=Semantic Decision Tables: Self-Organizing and Reorganizable Decision Tables |series=Proceedings of DEXA'08 (19th International Conference on Database and Expert Systems Applications) |publisher=Springer |id=LNCS 5181 |editor=S. Bhwmich |editor2=Josef Kung |editor3=Roland Wagner |place=Turin, Italy}}
• {{cite book |chapter=Use Semantic Decision Tables to Improve Meaning Evolution Support Systems |author1=Yan Tang |author2=Robert Meersman |name-list-style=amp |year=2009 |title=International Conference on Ubiquitous Intelligence and Computing |editor1=Frode Eika Sandnes |editor2=Yan Zhang |editor3=Chunming Rong |editor4=Laurence T. Yang |editor5=Jianhua Ma |display-editors=etal |doi=10.1007/978-3-540-69293-5_15 |isbn=978-3-540-69293-5}}
• {{cite book |title=SDRule Markup Language: Towards Modelling and Interchanging Ontological Commitments for Semantic Decision Making |author1=Yan Tang |author2=Robert Meersman |name-list-style=amp |series=Handbook of Research on Emerging Rule-Based Languages and Technologies: Open Solutions and Approaches |publisher=IGI Publishing, USA |isbn=978-1-60566-402-6 |year=2009}}

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