Natural language techniques supporting decision modelers

Abstract

Decision Model and Notation (DMN) has become a relevant topic for organizations since it allows users to control their processes and organizational decisions. The increasing use of DMN decision tables to capture critical business knowledge raises the need for supporting analysis tasks such as the extraction of inputs, outputs and their relations from natural language descriptions. In this paper, we create a stepping stone towards implementing a Natural Language Processing framework to model decisions based on the DMN standard. Our proposal contributes to the generation of decision rules and tables from a single sentence analysis. This framework comprises three phases: (1) discourse and semantic analysis, (2) syntactic analysis and (3) decision table construction. To the best of our knowledge, this is the first attempt devoted to automatically discovering decision rules according to the DMN terminology from natural language descriptions. Aiming at assessing the quality of the resultant decision tables, we have conducted a survey involving 16 DMN 2 Leticia Arco et al. experts. The results have shown that our framework is able to generate semantically correct tables. It is convenient to mention that our proposal does not aim to replace analysts but support them in creating better models with less effort.