Abstract
A modern design organization has a significant electronic archive of documents in an unstructured form. Solving the problem of using the experience of previous projects to solve new problems can be based on the use of intelligent methods and algorithms for analyzing text documents of an organization in order to build a classification system for electronic archives. This work presents an ontological model of a text document as an electronic archive resource. The paper also presents an ontologically oriented classification algorithm for technical documents. In conclusion, the results of experiments confirming the effectiveness of models and algorithms in solving the problem of classifying a document archive are presented.
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Acknowledgements
This paper has been approved within the framework of the federal target project “R&D for Priority Areas of the Russian Science-and-Technology Complex Development for 2014–2020”, government contract No 05.604.21.0252 on the subject “The development and research of models, methods and algorithms for classifying large semistructured data based on hybridization of semantic-ontological analysis and machine learning”.
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Zarubin, A., Koval, A., Moshkin, V. (2021). Classification of Text Documents of an Electronic Archive Based on an Ontological Model. In: Satapathy, S., Zhang, YD., Bhateja, V., Majhi, R. (eds) Intelligent Data Engineering and Analytics. Advances in Intelligent Systems and Computing, vol 1177. Springer, Singapore. https://doi.org/10.1007/978-981-15-5679-1_56
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DOI: https://doi.org/10.1007/978-981-15-5679-1_56
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