Semantic DMN: Formalizing Decision Models with Domain Knowledge

Proc. of the 1st Int. Joint Conf. on Rules and Reasoning (RuleML+RR 2017). Volume 10364 of Lecture Notes in Computer Science. 2017. Best paper award.

The Decision Model and Notation (DMN) is a recent OMG standard for the elicitation and representation of decision models. DMN builds on the notion of decision table, which consists of columns representing the inputs and outputs of a decision, and rows denoting rules. DMN models work under the assumption of complete information, and do not support integration with background domain knowledge. In this paper, we overcome these issues, by proposing decision knowledge bases (DKBs), where decisions are modeled in DMN, and domain knowledge is captured by means of first-order logic with datatypes. We provide a logic-based semantics for such an integration, and formalize how the different DMN reasoning tasks introduced in the literature can be lifted to DKBs. We then consider the case where background knowledge is expressed as an ALC description logic ontology equipped with datatypes, and show that in this setting, all reasoning tasks can be actually decided in ExpTime. We discuss the effectiveness of our framework on a case study in maritime security.

@inproceedings{RuleML-RR-2017,
   title = "Semantic DMN: Formalizing Decision Models with Domain
Knowledge",
   year = "2017",
   author = "Diego Calvanese and Marlon Dumas and Fabrizio M. Maggi and
Marco Montali",
   booktitle = "Proc. of the 1st Int. Joint Conf. on Rules and Reasoning
(RuleML+RR 2017)",
   pages = "70--86",
   volume = "10364",
   publisher = "Springer",
   series = "Lecture Notes in Computer Science",
   doi = "10.1007/978-3-319-61252-2  6",
   note = "Best paper award",
}