Semantic decision table

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Semantic decision tables (SDT) use modern ontology engineering (OE) technologies to enhance traditional decision tables. The term "semantic decision table" was coined by Yan Tang and Prof. Robert Meersman from VUB STARLab (Free University of Brussels) in 2006.[1] An SDT is a (set of) decision table(s) 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.


SDT is a 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".[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 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]; and it is time consuming to create and maintain large decision tables[citation needed]. Semantic decision tables are an attempt to solve these problems.


SDT 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, an SDT consists of a layer of the decision binary fact types called SDT lexons and a SDT commitment layer that consists of the constraints and axioms of these fact types.

A lexon l is a quintuple <γ,t1,r1,r2,t2>. t1 and t2 represent two concepts in a natural language (e.g. English); r1 and r2 (in, r1 corresponds to “role” and r2 - “co-role”) refer to the relationships that the concepts share with respect to one another; γ is a context identifier refers to a context, which serves to disambiguate the terms t1, t2 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 (ORM), conceptual graph (CG), and Unified Modeling Language (UML).

SDT model[edit]

An SDT 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 table(s). 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.


  1. ^ Yan Tang & Robert Meersman (2007). C. Man-chung; J.N.K. Liu; R. Cheung & J.Zhou, eds. Towards building semantic decision table with domain ontologies. Proc. of International conference of information Technology and Management (ICITM2007). ISM Press. pp. 14–21. ISBN 988-97311-5-0. 
  2. ^ Canadian Standards Association (1970). Z243.1-1970 for Decision Tables. 
  3. ^ Robert Meersman (2001). d'Atri, A.; Missikoff, M., eds. Ontologies and Databases:More than a Fleeting Resemblance. Proc. of OES/SEO 2001 Rome Workshop. Luiss Publication. 


  • Canadian Standards Association (1970). Z243.1-1970 for Decision Tables. 
  • Yan Tang & Robert Meersman (2007). C. Man-chung; J.N.K. Liu; R. Cheung & J. Zhou, eds. Towards building semantic decision table with domain ontologies. Proceedings of the International Conference of Information Technology and Management (ICITM2007). ISM Press. pp. 14–21. ISBN 988-97311-5-0. 
  • Yan Tang & Robert Meersman (2008). Man-Chung Chan; Ronnie Cheung & James N K Liu, eds. Towards Building Semantic Decision Tables with Domain Ontologies. Challenges in Information Technology Management. World Scientific. ISBN 978-981-281-906-2. 
  • Yan Tang, Robert Meersman and Jan Vanthienen. S. Bhwmich; Josef Kung; Roland Wagner, eds. Semantic Decision Tables: Self-Organizing and Reorganizable Decision Tables. Proceedings of DEXA'08 (19th International Conference on Database and Expert Systems Applications). Turin, Italy: Springer. LNCS 5181. 
  • Yan Tang & Robert Meersman (2009). "Use Semantic Decision Tables to Improve Meaning Evolution Support Systems". In Frode Eika Sandnes, Yan Zhang, Chunming Rong, Laurence T. Yang, Jianhua Ma et al. International Conference on Ubiquitous Intelligence and Computing. doi:10.1007/978-3-540-69293-5_15. ISBN 978-3-540-69293-5. 
  • Yan Tang & Robert Meersman (2009). SDRule Markup Language: Towards Modelling and Interchanging Ontological Commitments for Semantic Decision Making. Handbook of Research on Emerging Rule-Based Languages and Technologies: Open Solutions and Approaches. IGI Publishing, USA. ISBN 1-60566-402-2.