Authors: Paulius Danenas, Rimantas Butleris, Tomas Skersys
Tags: 2020, conceptual modeling
Discovery, specification and proper representation of various aspects of business knowledgeplays crucial part in model-driven information systems engineering, especially when it comesto the early stages of systems development. Being among the most applicable and advancedfeatures of model-driven development, model transformation could help improving one of themost time- and resource-consuming efforts in this process, namely, discovery and specificationof business vocabularies and business rules within the problem domain. One of our latestdevelopments in this area was the solution for the automatic extraction of SBVR businessvocabularies and business rules from UML use case diagrams, which was arguably one of themost comprehensive developments of this kind currently available in public. In this paper, wepresent an enhancement to our previous development by introducing a novel natural languageprocessing component to it. This enhancement provides more advanced extraction capabilities(such as recognition of entities, entire noun and verb phrases, multinary associations) and betterquality of the extraction results compared to our previous solution. The main contributionspresented in this paper are pre- and post-processing algorithms, and two extraction algorithmsusing custom-trained POS tagger. Based on the related work findings, it is safe to state thatthe presented solution is novel and original in its approach of combining together M2Mtransformation of UML and SBVR models with natural language processing techniques in thefield of model-driven information systems engineering.Read the full paper here: https://reader.elsevier.com/reader/sd/pii/S0169023X1930299X?token=75D26216017B598DA0012ADE8B8CE60522B80C660ACD5D62576B75A8CF3FE157C75516E01D7B07C423F349D096C994BF
