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ItemIdentification of Decision Rules from Legislative Documents Using Machine Learning and Natural Language Processing( 2022-01-04)Decision logic extraction from natural language texts can be a tedious, labor-intensive task. This is especially true for legislative texts, since they do not always follow usual speech and writing patterns. This paper explores the possibility of using machine learning and natural language processing approaches to identify decision rules within legislative documents, and ultimately provides the possibility of building an extraction algorithm on top of the solution to extract and visualize decision logic automatically. Such a novel method for decision rules identification bears the potential to reduce human labor, minimize mistakes, and lessen context dependency. To accomplish this, we use pre-trained word vectorization in conjunction with a complex multi-layer convolutional neural network (CNN). The relevant data used in this project was generated from the Austrian income tax code and labeled by hand. A quantitative evaluation shows that our approach can be trained on as little as a single code of law and still obtain significant accuracy.
ItemContext-Aware Verification of DMN( 2022-01-04)The Decision Model and Notation (DMN) standard is a user-friendly notation for decision logic. To verify correctness of DMN decision tables, many tools are available. However, most of these look at a table in isolation, with little or no regards for its context. In this work, we argue for the importance of context, and extend the formal verification criteria to include it. We identify two forms of context, namely in-model context and background knowledge. We also present our own context-aware verification tool, implemented in our DMN-IDP interface, and show that this context-aware approach allows us to perform more thorough verification than any other available tool.
ItemBusiness Rules Management and Decision Mining - Filling in the Gaps( 2022-01-04)Proper decision-making is one of the most important capabilities of an organization. Adequately managing these decisions is therefore of high importance. Business Rules Management (BRM) is an approach that helps in managing decisions and underlying business logic. However, questions still arise if the decisions are properly improved based on decision data. Decision Mining (DM) could complement BRM capabilities in order to improve towards effective and efficient decision-making. In this study, we propose the integration of BRM and DM through a simulation using a government and a healthcare case. During this simulation, three entry points are presented that describe how decision-related data should be utilized between BRM capabilities and DM phases to be able to integrate them. The presented results provide a basis from which more technical research on the three DM phases can be further explored.