{"title":"Graphical Transformation of OWL Ontologies to Event-B Formal Models","authors":"Eman H. Alkhammash","doi":"10.32604/CMC.2022.015987","DOIUrl":null,"url":null,"abstract":": Formal methods use mathematical models to develop systems. Ontologies are formal specifications that provide reusable domain knowledge representations. Ontologies have been successfully used in several data-driven applications, including data analysis. However, the creation of formal models from informal requirements demands skill and effort. Ambiguity, incon-sistency, imprecision, and incompleteness are major problems in informal requirements. To solve these problems, it is necessary to have methods and approaches for supporting the mapping of requirements to formal specifications. The purpose of this paper is to present an approach that addresses this challenge by using the Web Ontology Language (OWL) to construct Event-B formal models and support data analysis. Our approach reduces the burden of working with the formal notations of OWL ontologies and Event-B models and aims to analyze domain knowledge and construct Event-B models from OWL ontologies using visual diagrams. The idea is based on the transformation of OntoGraf diagrams of OWL ontologies to UML-B diagrams for the purpose of bridging the gap between OWL ontologies and Event-B models. Visual data exploration assists with both data analysis and the development of Event-B formal models. To manage complexity, Event-B supports stepwise refinement to allow each requirement to be introduced at the most appropriate stage in the development process. UML-B supports refinement, so we also introduce an approach that allows us to divide and layer OntoGraf diagrams.","PeriodicalId":10440,"journal":{"name":"Cmc-computers Materials & Continua","volume":"90 1","pages":"3733-3750"},"PeriodicalIF":2.0000,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Cmc-computers Materials & Continua","FirstCategoryId":"94","ListUrlMain":"https://doi.org/10.32604/CMC.2022.015987","RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"COMPUTER SCIENCE, INFORMATION SYSTEMS","Score":null,"Total":0}
引用次数: 1
Abstract
: Formal methods use mathematical models to develop systems. Ontologies are formal specifications that provide reusable domain knowledge representations. Ontologies have been successfully used in several data-driven applications, including data analysis. However, the creation of formal models from informal requirements demands skill and effort. Ambiguity, incon-sistency, imprecision, and incompleteness are major problems in informal requirements. To solve these problems, it is necessary to have methods and approaches for supporting the mapping of requirements to formal specifications. The purpose of this paper is to present an approach that addresses this challenge by using the Web Ontology Language (OWL) to construct Event-B formal models and support data analysis. Our approach reduces the burden of working with the formal notations of OWL ontologies and Event-B models and aims to analyze domain knowledge and construct Event-B models from OWL ontologies using visual diagrams. The idea is based on the transformation of OntoGraf diagrams of OWL ontologies to UML-B diagrams for the purpose of bridging the gap between OWL ontologies and Event-B models. Visual data exploration assists with both data analysis and the development of Event-B formal models. To manage complexity, Event-B supports stepwise refinement to allow each requirement to be introduced at the most appropriate stage in the development process. UML-B supports refinement, so we also introduce an approach that allows us to divide and layer OntoGraf diagrams.
期刊介绍:
This journal publishes original research papers in the areas of computer networks, artificial intelligence, big data management, software engineering, multimedia, cyber security, internet of things, materials genome, integrated materials science, data analysis, modeling, and engineering of designing and manufacturing of modern functional and multifunctional materials.
Novel high performance computing methods, big data analysis, and artificial intelligence that advance material technologies are especially welcome.