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12 November 2024 Knowledge graph construction method for business process instructed by prompts
Shijia Gu, Yujia Qi
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Proceedings Volume 13395, International Conference on Optics, Electronics, and Communication Engineering (OECE 2024) ; 133953X (2024) https://doi.org/10.1117/12.3049117
Event: International Conference on Optics, Electronics, and Communication Engineering, 2024, Wuhan, China
Abstract
The technology of generative general artificial intelligence not only revolutionizes machine intelligence but also plays a significant role in enterprise digitalization. Given that natural gas sales companies offer conventional human-centered services, cost and efficiency have emerged as a set of irreconcilable contradictions. The automated intelligent customer service model, based on the new generation of artificial intelligence technology, is like an open key to high-quality development. We propose a method to create a knowledge graph of customer service business processes by guiding a multi-modal large language model to generate the knowledge graph with prompt words. Based on the open-source multimodal large language model, the general ability of the large model was applied to identify, analyze, and extract the business process table in the “Natural Gas Customer Standardized Service Business Process Guidebook” through customized task prompt design. Ultimately, we successfully constructed a business process knowledge graph, confirming the feasibility and effectiveness of this method. This method aims to optimize intelligent customer service by providing high-quality answers. The method enhances automation, intelligence, and standardization of customer service, which improves work efficiency and controls costs simultaneously.
(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.
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Shijia Gu and Yujia Qi "Knowledge graph construction method for business process instructed by prompts", Proc. SPIE 13395, International Conference on Optics, Electronics, and Communication Engineering (OECE 2024) , 133953X (12 November 2024); https://doi.org/10.1117/12.3049117
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KEYWORDS
Data modeling
Image processing
Design
Intelligence systems
Engineering
Visualization
Machine learning
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