Semantic requirements construction using ontologies and boilerplates

IF 2.7 3区 计算机科学 Q3 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Data & Knowledge Engineering Pub Date : 2024-05-28 DOI:10.1016/j.datak.2024.102323
Christina Antoniou, Kalliopi Kravari, Nick Bassiliades
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Abstract

This paper presents a combination of an ontology and boilerplates, which are requirements templates for the syntactic structure of individual requirements that try to alleviate the problem of ambiguity caused using natural language and make it easier for inexperienced engineers to create requirements. However, still the use of boilerplates restricts the use of natural language only syntactically and not semantically. Boilerplates consists of fixed and attributes elements. Using ontologies, restricts the vocabulary of the words used in the requirements boilerplates to entities, their properties and entity relationships that are semantically meaningful to the application domain, leading thus to fewer errors. In this work we combine the advantages of boilerplates and ontologies. Usually, the attributes of boilerplates are completed with the help of the ontology. The contribution of this paper is that the whole boilerplates are stored in the ontology, based on the fact that RDF triples have similar syntax to the boilerplate syntax, so that attributes and fixed elements are part of the ontology. This combination helps to construct semantically and syntactically correct requirements. The contribution and novelty of our method is that we exploit the natural language syntax of boilerplates mapping them to Resource Description Framework triples which have also a linguistic nature. In this paper we created and present the development of a domain-specific ontology as well as a minimal set of boilerplates for a specific application domain, namely that of engineering software for an ATM, while maintaining flexibility on the one hand and generality on the other.

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使用本体和模板构建语义需求
本文介绍了本体与模板的结合,后者是针对单个需求的语法结构的需求模板,试图缓解使用自然语言造成的歧义问题,并使缺乏经验的工程师更容易创建需求。然而,模板的使用仍然只限制了自然语言在语法上的使用,而没有限制其在语义上的使用。模板由固定元素和属性元素组成。使用本体可以将需求模板中使用的词汇限制在对应用领域有语义意义的实体、实体属性和实体关系上,从而减少错误。在这项工作中,我们结合了模板和本体的优点。通常,模板的属性是在本体的帮助下完成的。本文的贡献在于,基于 RDF 三元组具有与模板语法相似的语法这一事实,将整个模板存储在本体中,从而使属性和固定元素成为本体的一部分。这种组合有助于构建语义和语法正确的需求。我们的方法的贡献和新颖之处在于,我们利用了模板的自然语言语法,将其映射到同样具有语言性质的资源描述框架三元组中。在本文中,我们创建并介绍了针对特定应用领域(即自动取款机的工程软件)的领域本体和最小模板集的开发过程,同时保持了灵活性和通用性。
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来源期刊
Data & Knowledge Engineering
Data & Knowledge Engineering 工程技术-计算机:人工智能
CiteScore
5.00
自引率
0.00%
发文量
66
审稿时长
6 months
期刊介绍: Data & Knowledge Engineering (DKE) stimulates the exchange of ideas and interaction between these two related fields of interest. DKE reaches a world-wide audience of researchers, designers, managers and users. The major aim of the journal is to identify, investigate and analyze the underlying principles in the design and effective use of these systems.
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