Development of Automated URL Correction using Azure AI Service

Abstract

Development of the Automated URL Validation, Status Monitoring and Correction using Azure AI Service aims to enhance the efficiency of end-user access to library resources. This allows users to access correctly and swiftly by adjusting the URL verifying process of library resources on WebOPAC from manual to automatic operation. As a result, it is reducing time and steps in the operational process. Such optimizations collectively serve to enhance overall service effectiveness. The system developers employ principles and processes in accordance with the System Development Life Cycle (SDLC), employing Azure AI Service and utilizing Python3 for program development. From the operational results, it was found that the system helps reduce the steps and time in the operational process in the following aspects: 1. Verifying the correctness of the linking format, which arises from errors made by data entry personnel. 2. Checking the status of the URL and updating new sources, the entire process takes an average of 1-2 seconds per item. Furthermore, this system is not limited to library systems but may be applied to a variety of operations or systems.  As an example, Digital Repositories, Journal Systems, and even website links to various web pages can be benefited from this system without the necessity allocating budgets for system development or procurement.