Contextualization of Information Objects Towards Supporting Knowledge Management in Digital Workspaces
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 in...
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| Format: | Article |
| Language: | English |
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Riga Technical University Press
2024-10-01
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| Series: | Complex Systems Informatics and Modeling Quarterly |
| Subjects: | |
| Online Access: | https://csimq-journals.rtu.lv/article/view/8980 |
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| _version_ | 1832542982201409536 |
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| author | Mara Romanovska Ilze Birzniece Signe Balina |
| author_facet | Mara Romanovska Ilze Birzniece Signe Balina |
| author_sort | Mara Romanovska |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-d93c9f4189df4dac90ba02beb38fa83c |
| institution | Kabale University |
| issn | 2255-9922 |
| language | English |
| publishDate | 2024-10-01 |
| publisher | Riga Technical University Press |
| record_format | Article |
| series | Complex Systems Informatics and Modeling Quarterly |
| spelling | doaj-art-d93c9f4189df4dac90ba02beb38fa83c2025-02-03T12:03:20ZengRiga Technical University PressComplex Systems Informatics and Modeling Quarterly2255-99222024-10-01040365710.7250/csimq.2024-40.023739Contextualization of Information Objects Towards Supporting Knowledge Management in Digital WorkspacesMara Romanovska0Ilze Birzniece1Signe Balina2Institute of Applied Computer Systems, Riga Technical University, 6A Kipsalas Street, Riga, LV-1048, LatviaInstitute of Applied Computer Systems, Riga Technical University, 6A Kipsalas Street, Riga, LV-1048, LatviaDatorzinibu centrs, Riga, LV-1011, LatviaThis 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.https://csimq-journals.rtu.lv/article/view/8980context-aware computingcontext modelingcontext attributesorganizational knowledge managementpersonalized digital workspaceartificial intelligencelarge language models |
| spellingShingle | Mara Romanovska Ilze Birzniece Signe Balina Contextualization of Information Objects Towards Supporting Knowledge Management in Digital Workspaces Complex Systems Informatics and Modeling Quarterly context-aware computing context modeling context attributes organizational knowledge management personalized digital workspace artificial intelligence large language models |
| title | Contextualization of Information Objects Towards Supporting Knowledge Management in Digital Workspaces |
| title_full | Contextualization of Information Objects Towards Supporting Knowledge Management in Digital Workspaces |
| title_fullStr | Contextualization of Information Objects Towards Supporting Knowledge Management in Digital Workspaces |
| title_full_unstemmed | Contextualization of Information Objects Towards Supporting Knowledge Management in Digital Workspaces |
| title_short | Contextualization of Information Objects Towards Supporting Knowledge Management in Digital Workspaces |
| title_sort | contextualization of information objects towards supporting knowledge management in digital workspaces |
| topic | context-aware computing context modeling context attributes organizational knowledge management personalized digital workspace artificial intelligence large language models |
| url | https://csimq-journals.rtu.lv/article/view/8980 |
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