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Semi-automated development of conceptual models from natural language text

机译：自然语言文本的半自动开发概念模型

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摘要

The process of converting natural language specifications into conceptual models requires detailed analysis of natural language text, and designers frequently make mistakes when undertaking this transformation manually. Although many approaches have been used to partly automate this process, one of the main limitations is the lack of a domain-independent ontology that can be used as a repository for entities and relationships, thus guiding the transformation process. In this paper, a semi-automated system for mapping natural language text into conceptual models is proposed. The system, called SACMES, combines a linguistic approach with an ontological approach and human intervention to achieve the task. SACMES learns from the natural language specifications that it processes and stores the information that is learnt in a conceptual model ontology and a user history knowledge database. It then uses the stored information to improve performance and reduce the need for human intervention. The evaluation conducted on SACMES demonstrates that (1) by using the system, precision and recall for users identifying entities of conceptual models is increased by 6% and 13%, respectively, while for relationships, increases are even higher, 14% for precision and 23% for recall; (2) the performance of the system is improved by processing more natural language requirements, and thus, the need for human intervention is decreased.
机译：将自然语言规范转换为概念模型的过程需要对自然语言文本进行详细分析，手动在进行此转换时经常犯错误。虽然已经使用了许多方法来部分自动化此过程，但其中一个主要限制是缺少域 - 独立于域的本体，这些本体可以用作实体和关系的存储库，从而引导转换过程。本文提出了一种用于将自然语言文本映射到概念模型的半自动系统。该系统称为Sacmes，将语言方法与本体论方法和人为干预相结合，以实现任务。 Sacmes从IT流程的自然语言规范中学习并存储在概念模型本体和用户历史知识数据库中学习的信息。然后，它使用存储的信息来提高性能并减少对人类干预的需求。 Sacmes上进行的评估表明（1）通过使用该系统，识别概念模型实体的用户的精确和召回分别增加了6％和13％，而对于关系，增加甚至更高，14％召回23％; （2）通过处理更自然的语言要求，系统的性能得到改善，因此，对人类干预的需求降低。

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