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This research article explores the contextualization of information objects in enhancing knowledge management within digital workspaces. It emphasizes the critical role of context in managing unstructured data and presents a systematic approach for extracting context dimensions and attributes for information object context modeling. The research article discusses several implications of context-aware computing for organizational productivity: efficient information retrieval, improved knowledge management, support for remote and hybrid work models, reduced data loss, enhanced user activities, and business-level services. The article emphasizes the CASAD matrix modeling method and proposes the approach of extracting the set of attributes for building a context model. A case study is presented to demonstrate the practical application of this approach in an organizational setting. It is shown how combining large language models (LLMs) and organization-specific metamodels contributes to computing secondary context attributes. The research concludes that contextualizing information objects, supported by artificial intelligence and LLMs, can enhance organizational productivity by providing a ground for personalized digital workspaces, efficient information handling, and improved knowledge management processes. It can also support the evolving needs of organizations, such as remote and hybrid work models. |
