Revolutionizing knowledge organization with machine learning for metadata, classification, and retrieval in libraries
DOI:
https://doi.org/10.29352/mill0223e.37496Keywords:
classification; information retrieval; knowledge organization; machine learning; metadataAbstract
Introduction: This paper is going to explore how machine learning transformations will take library modernization a step further and help in knowledge organisation methods for better information retrieval processes.
Objective: To discuss how to make use of techniques in ML to develop the required library functions like metadata creation, classification systems, and information retrieval in an efficient and accurate manner.
Methods: The techniques used are based on a thorough knowledge of different machine learning algorithms, such as NLP for metadata generation and neural networks for classification.
Results: The case studies indeed prove that these techniques have been applied to and implemented in real library systems, as revealed by major libraries that have begun to implement ML technologies. These deployments lead to more efficiency, accuracy, and user experience. There are also issues highlighted in this research that could be problematic: biased training data, and transparency in algorithmic decisions.
Conclusion: There is a lot of promise in the future of libraries with the use of ML, and integration should be guided with a judicious balance, taking care of ethical issues.
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