Discovery, specification and proper representation of various aspects of business knowledge plays crucial part in model-driven information systems engineering, especially when it comes to the early stages of systems development. Being among the most applicable and advanced features of model-driven development, model transformation could help improving one of the most time- and resource-consuming efforts in this process, namely, discovery and specification of business vocabularies and business rules within the problem domain. One of our latest developments in this area was the solution for the automatic extraction of SBVR business vocabularies and business rules from UML use case diagrams, which was arguably one of the most comprehensive developments of this kind currently available in public. In this paper, we present an enhancement to our previous development by introducing a novel natural language processing component to it. This enhancement provides more advanced extraction capabilities (such as recognition of entities, entire noun and verb phrases, multinary associations) and better quality of the extraction results compared to our previous solution. The main contributions presented in this paper are pre- and post-processing algorithms, and two extraction algorithms using custom-trained POS tagger. Based on the related work findings, it is safe to state that the presented solution is novel and original in its approach of combining together M2M transformation of UML and SBVR models with natural language processing techniques in the field of model-driven information systems engineering.

商业知识各个方面的发现，规范和正确表示在模型驱动的信息系统工程中至关重要，尤其是在系统开发的早期阶段。作为模型驱动开发的最适用和最先进的功能之一，模型转换可以帮助改进此过程中最耗时和最耗资源的工作之一，即发现和指定问题域内的业务词汇和业务规则。我们在该领域的最新发展之一是从UML用例图中自动提取SBVR业务词汇和业务规则的解决方案，可以说这是目前公开可用的最全面的此类开发之一。在本文中，通过向其引入新颖的自然语言处理组件，我们对其进行了改进。与我们以前的解决方案相比，此增强功能提供了更高级的提取功能（例如，实体的识别，整个名词和动词短语，多元关联）以及更好的提取结果质量。本文提出的主要贡献是预处理和后处理算法，以及两种使用定制训练的POS标记器的提取算法。基于相关的工作发现，可以肯定地说，在将模型驱动的信息系统工程领域中的UML和SBVR模型的M2M转换与自然语言处理技术结合起来的情况下，提出的解决方案是新颖而新颖的。