To support decision making, organizations tend to operate according to Business Rules, which are usually represented in a natural language format easily understood by all intervenors. According to the business rules manifesto by the Business Rules Group (OMG), rules build on facts, and facts build on concepts as expressed by terms. To avoid ambiguity and misunderstanding, the standardization of the terminology used at the business level becomes a persistent need. However, doing so manually is error prone and time consuming, especially that the Business Rules are the subject of continuous updating. In this paper, we present an automated approach to generate the Business Vocabulary from textual statements of Business Rules. Our approach is distinguished from existing works in that it extracts the Terminological Dictionary as described by the Semantic of Business Vocabulary and Rules (SBVR) standard to provide a more comprehensive meaning for each concept. Accordingly, an in‐depth Natural Language Processing (NLP) is used to extract not only flat list of terms and relations, but also extra specifications and implicit knowledge. With a satisfactory result, our approach has proved its capability to automatically generate the SBVR Terminological Dictionary from large number of natural language business rules statements.

中文翻译：

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根据文本业务规则自动生成术语词典

为了支持决策，组织倾向于根据业务规则进行操作，通常以所有干预者易于理解的自然语言格式表示业务规则。根据业务规则组（OMG）的业务规则宣言，规则以事实为基础，事实以术语表达的概念为基础。为了避免歧义和误解，在业务级别使用的术语的标准化成为一个持续的需求。但是，手动执行操作容易出错且耗时，尤其是业务规则是不断更新的主题。在本文中，我们提出了一种自动方法，可以根据业务规则的文本陈述生成业务词汇表。我们的方法与现有作品的不同之处在于，它提取了商务词汇和规则语义（SBVR）标准所描述的术语词典，为每个概念提供了更全面的含义。因此，深入的自然语言处理（NLP）不仅用于提取术语和关系的平面列表，而且还用于提取额外的规范和隐式知识。取得令人满意的结果，我们的方法证明了它能够从大量自然语言业务规则语句中自动生成SBVR术语词典的能力。而且还有额外的规范和隐性知识。取得令人满意的结果，我们的方法证明了它能够从大量自然语言业务规则语句中自动生成SBVR术语词典的能力。而且还有额外的规范和隐性知识。取得令人满意的结果，我们的方法证明了它能够从大量自然语言业务规则语句中自动生成SBVR术语词典的能力。