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|Booktitle=Proceedings of the 6th International Conference on Model-Driven Engineering and Software Development – Volume 1: MODELSWARD | |Booktitle=Proceedings of the 6th International Conference on Model-Driven Engineering and Software Development – Volume 1: MODELSWARD | ||
+ | |Pages=71-82 | ||
|Publisher=SCITEPRESS | |Publisher=SCITEPRESS | ||
}} | }} | ||
{{Publikation Details | {{Publikation Details | ||
|Abstract=Domain modeling is an important activity in early phases of software projects to achieve a shared understanding of the problem field among project participants. Domain models describe concepts and relations of respective application fields using a modeling language and domain-specific terms. Detailed knowledge of the domain as well as expertise in model-driven development is required for software engineers to create these models. This paper describes DoMoRe, a system for automated modeling recommendations to support the domain modeling process. We describe an approach in which modeling benefits from formalized knowledge sources and information extraction from text. The system incorporates a large network of semantically related terms built from natural language data sets integrated with mediator-based knowledge base querying in a single recommender system to provide context-sensitive suggestions of model elements. | |Abstract=Domain modeling is an important activity in early phases of software projects to achieve a shared understanding of the problem field among project participants. Domain models describe concepts and relations of respective application fields using a modeling language and domain-specific terms. Detailed knowledge of the domain as well as expertise in model-driven development is required for software engineers to create these models. This paper describes DoMoRe, a system for automated modeling recommendations to support the domain modeling process. We describe an approach in which modeling benefits from formalized knowledge sources and information extraction from text. The system incorporates a large network of semantically related terms built from natural language data sets integrated with mediator-based knowledge base querying in a single recommender system to provide context-sensitive suggestions of model elements. | ||
+ | |ISBN=978-989-758-283-7 | ||
+ | |Download=2018-MODELSWARD-19.pdf | ||
|Link=http://www.scitepress.org/DigitalLibrary/Link.aspx?doi=10.5220/0006555700710082 | |Link=http://www.scitepress.org/DigitalLibrary/Link.aspx?doi=10.5220/0006555700710082 | ||
− | |DOI Name= | + | |DOI Name=10.5220/0006555700710082 |
|Forschungsgruppe=Information Service Engineering | |Forschungsgruppe=Information Service Engineering | ||
}} | }} |
Aktuelle Version vom 17. November 2022, 14:53 Uhr
DoMoRe -- A Recommender System for Domain Modeling
DoMoRe -- A Recommender System for Domain Modeling
Published: 2018
Januar
Buchtitel: Proceedings of the 6th International Conference on Model-Driven Engineering and Software Development – Volume 1: MODELSWARD
Seiten: 71-82
Verlag: SCITEPRESS
Referierte Veröffentlichung
BibTeX
Kurzfassung
Domain modeling is an important activity in early phases of software projects to achieve a shared understanding of the problem field among project participants. Domain models describe concepts and relations of respective application fields using a modeling language and domain-specific terms. Detailed knowledge of the domain as well as expertise in model-driven development is required for software engineers to create these models. This paper describes DoMoRe, a system for automated modeling recommendations to support the domain modeling process. We describe an approach in which modeling benefits from formalized knowledge sources and information extraction from text. The system incorporates a large network of semantically related terms built from natural language data sets integrated with mediator-based knowledge base querying in a single recommender system to provide context-sensitive suggestions of model elements.
ISBN: 978-989-758-283-7
Download: Media:2018-MODELSWARD-19.pdf
Weitere Informationen unter: Link
DOI Link: 10.5220/0006555700710082
Information Service Engineering