Ontologies for development and generation adaptive user interfaces of knowledge base editors
Автор: Gribova V.V., Parshkova S.V., Fedorischev L.A.
Журнал: Онтология проектирования @ontology-of-designing
Рубрика: Инжиниринг онтологий
Статья в выпуске: 2 (44) т.12, 2022 года.
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The article discusses a method for creating automatically generated adaptive user interfaces of knowledge base editors based on an ontological approach. The main goal that guided the authors is to improve the quality and convenience of creating and editing knowledge bases, taking into account the specifics of the subject area, the characteristics of an expert user and other parameters. The description of the concept of a new approach to generating adaptive interfaces is given. Additionally, a detailed description of all ontologies, knowledge bases and models used in the specified approach to generating interfaces, as key elements of the proposed idea. The presented ontology of knowledge about interface design introduces new abstract elements that define interface tasks without explicitly defining their visual and functional representation, while each abstract element of this ontology has several representation options - adaptations of abstract elements. The ontology of graphical elements of the user interface defines a specific visual and functional representation of abstract elements in the form of ready-made reusable WIMP elements, such as lists, buttons, menus, etc. The presented knowledge base on interface design describes the rules for its formation depending on the structure of the domain ontology, user characteristics, and usability requirements. The presented interface model describes the structure of the generated adaptive interface in accordance with the user's individual preferences and other additional information. All the ontologies and knowledge bases described in the article are currently implemented on the IACPaaS platform.
Ontology, knowledge base, interface, adaptive interface, interface generation, templates, wimp interface, knowledge base editor
Короткий адрес: https://sciup.org/170195100
IDR: 170195100 | DOI: 10.18287/2223-9537-2022-12-2-200-217