Semantic DMN: Formalizing and Reasoning About Decisions in the Presence of Background Knowledge
Published online by Cambridge University Press: 18 January 2019
The Decision Model and Notation (DMN) is a recent Object Management Group standard for the elicitation and representation of decision models and for managing their interconnection with business processes. DMN builds on the notion of decision tables and their combination into more complex decision requirements graphs (DRGs), which bridge between business process models and decision logic models. DRGs may rely on additional, external business knowledge models, whose functioning is not part of the standard. In this work, we consider one of the most important types of business knowledge, namely, background knowledge that conceptually accounts for the structural aspects of the domain of interest, and propose decision knowledge bases (DKBs), which semantically combine DRGs modeled in DMN, and domain knowledge captured by means of first-order logic with datatypes. We provide a logic-based semantics for such an integration, and formalize different DMN reasoning tasks for DKBs. We then consider background knowledge formulated as a description logic (DL) ontology with datatypes, and show how the main verification tasks for DMN in this enriched setting can be formalized as standard DL reasoning services and actually carried out in ExpTime. We discuss the effectiveness of our framework on a case study in maritime security.
- Original Article
- © Cambridge University Press 2019
This research is partly supported by the Estonian Research Council Grant IUT20-55, by the project “Reasoning and Enactment for Knowledge-Aware Processes” (REKAP), which is funded through the 2017 call issued by the Research Committee of the Free University of Bozen-Bolzano, and by the Euregio Interregional Project Network IPN12 “Knowledge-Aware Operational Support” (KAOS), which is funded by the “European Region Tyrol-South Tyrol-Trentino” (EGTC) under the first call for basic research projects and by the Free University of Bozen-Bolzano. This is an extended version of a paper presented at the RuleML+RR 2017 conference, which has been invited for submission to TPLP. The authors acknowledge the assistance of the RuleML+RR 2017 Program Chairs Stefania Costantini, Enrico Franconi, Fariba Sadri, and William van Woensel.