From Text to Intelligent Services in Knowledge Intensive Decision Processes: Text2Chat
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Date
2024-01-03
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5992
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Abstract
Knowledge-intensive processes intertwine processes and decisions. Such processes often end whenever the final decision outcome has been obtained and communicated to the stakeholder. However, stakeholders often desire additional clarification on decision outcomes which requires processing the decision and process information. The decision logic is often described in various internal and external textual documents but are often difficult to interpret. There exist various ways to generate and provide advice and explanation based on textual descriptions. We present an overview of existing research fragments and new research on integrating the pieces into a framework. This paper presents the Text2Chat framework for generating advice and explanation from the process description text, containing various tracks. Each track is explained together with its advantages and disadvantages. By introducing Text2Chat, this paper offers insights into processing textual knowledge-intensive process information for effective stakeholder advice provision.
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Business Process Technology, decision modeling, deep learning, explainability, knowledge-intensive processes, large language models
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10 pages
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Proceedings of the 57th Hawaii International Conference on System Sciences
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Attribution-NonCommercial-NoDerivatives 4.0 International
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