The method of linear-logical operators and logical equations in information extraction tasks
DOI:
https://doi.org/10.30837/ITSSI.2024.27.081Keywords:
knowledge bases; intelligent systems; algebra of finite predicates; logical operators; quantile linear equations.Abstract
Relational and logical methods of knowledge representation play a key role in creating a mathematical basis for information systems. Predicate algebra and predicate operators are among the most effective tools for describing information in detail. These tools make it easy to formulate formalized information, create database queries, and simulate human activity. In the context of the new need for reliable and efficient data selection, a problem arises in deeper analysis. Subject of the study is the theory of quantum linear equations based on the algebra of linear predicate operations, the formal apparatus of linear logic operators and methods for solving logical equations in information extraction tasks. Aim of the study is a developing of a method for using linear logic operators and logical equations to extract information. This approach can significantly optimize the process of extracting the necessary information, even in huge databases. Main tasks: analysis of existing approaches to information extraction; consideration of the theory of linear logic operators; study of methods for reducing logic to an algebraic form; analysis of logical spaces and the algebra of finite predicate actions and the theory of linear logic operators. The research methods involve a systematic analysis of the mathematical structure of the algebra of finite predicates and predicate functions to identify the key elements that affect the query formation process. The method of using linear logic operators and logical equations for information extraction is proposed. The results of the study showed that the method of using linear logic operators and logical equations is a universal and adaptive tool for working with algebraic data structures. It can be applied in a wide range of information extraction tasks and prove its value as one of the possible methods of information processing. Conclusion. The paper investigates formal methods of intelligent systems, in particular, ways of representing knowledge in accordance with the peculiarities of the field of application and the language that allows encoding this knowledge for storage in computer memory. The proposed method can be implemented in the development of language interfaces for automated information access systems, in search engine algorithms, for logical analysis of information in databases and expert systems, as well as in performing tasks related to object recognition and classification.
References
Список літератури
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References
Ahmad, A.Y. A. B., Kumari, D.K., Shukla, A., Deepak, A., Chandnani, M., Pundir, S., Shrivastava, A. (2024), "Framework for Cloud Based Document Management System with Institutional Schema of Database". International Journal of Intelligent Systems and Applications in Engineering. No. 12(3s), Р. 672–678, available at: https://ijisae.org/index.php/IJISAE/article/view/3853 (last accessed 12.03.2024).
Yang, X., Guan, X., Pang, Z., Kui, X., Wu, H. (2024), "GridMesa: A NoSQL-based big spatial data management system with an adaptive grid approximation model". Future Generation Computer Systems. Vol 155, Р. 324–339. DOI: https://doi.org/10.1016/j.future.2024.02.010
Taipalus, T. (2024), "Vector database management systems: Fundamental concepts, use-cases, and current challenges". Cognitive Systems Research. Vol. 85. 13 р. DOI: https://doi.org/10.1016/j.cogsys.2024.101216
Davydovskiy, M. (2023), "Storing of Lua tables in relational databases". In: AIP Conference Proceedings. DOI: https://doi.org/10.1063/5.0132449
Aishwarya, V. (2023), "A Prompt Engineering Approach for Structured Data Extraction from Unstructured Text Using Conversational LLMs". In: ACM International Conference Proceeding Series. Р. 183–189. DOI: https://doi.org/10.1145/3639631.3639663
Aebeloe, C., Montoya, G., Hose, K. (2023), "Optimizing SPARQL queries over decentralized knowledge graphs". Semantic Web. No. 14(6), Р. 1121–1165. DOI: https://doi.org/10.3233/SW-233438
Braun, C. H. J., Käfer, T. (2023), "Quantifiable integrity for Linked Data on the web". Semantic Web. No. 14(6), Р. 1167–1207. DOI: https://doi.org/10.3233/SW-233409
Faltín, T., Trigonakis, V., Berdai, A., Fusco, L., Iorgulescu, C., Lee, J., Yaghob, J., Hong, S., Chafi, H. (2023), "Distributed Asynchronous Regular Path Queries (RPQs) on Graphs". In: Middleware Industrial Track 2023 – Proceedings of the 2023 24th International Middleware Conference Industrial Track, Part of: Middleware 2023. Р. 35–41. DOI: https://doi.org/10.1145/3626562.3626833
Iglesias-Molina, A., Toledo, J., Corcho, O., Chaves-Fraga, D. (2023), "Re-Construction Impact on Metadata Representation Models". In: K-CAP 2023 – Proceedings of the 12th Knowledge Capture Conference 2023. Р. 197–205. DOI: https://doi.org/10.1145/3587259.3627554
Zykin, S.V. (2023), "Testing Dependencies and Inference Rules in Databases". Automatic Control and Computer Sciences. 57(7), Р. 788–802. DOI: https://doi.org/10.3103/S0146411623070179
Satheesh, A., Kumar, A. (2022), "An Object-Oriented Database Design for Effective Classification". International Journal of Intelligent Systems and Applications in Engineering. No. 10(4), Р. 111–119, available at: https://ijisae.org/index.php/IJISAE/article/view/2204 (last accessed 12.03.2024).
Figallo, M., Monica-Gomes, C. (2023), "The Subalgebra Lattice of a Finite Diagonal-Free Two-Dimensional Cylindric Algebra". Computación y Sistemas. No. 27(1). DOI: https://doi.org/10.13053/cys-27-1-4544
Yang, T., Wang, Y., Sha, L., Engelbrecht, J., Hong, P. (2022), "Knowledgebra: An Algebraic Learning Framework for Knowledge Graph". Machine Learning and Knowledge Extraction. No. 4(2), Р. 432–445. DOI: https://doi.org/10.3390/make4020019
Gilray, T., Kumar, S. (2019), "Distributed Relational Algebra at Scale". In: 2019 IEEE 26th International Conference on High Performance Computing, Data, and Analytics (HiPC), 17–20 December 2019, Hyderabad, India. IEEE. DOI: https://doi.org/10.1109/hipc.2019.00014
Luo, S., Gao, Z.J., Gubanov, M., Perez, L.L., Jermaine, C. (2019), "Scalable Linear Algebra on a Relational Database System". IEEE Transactions on Knowledge and Data Engineering. No. 31(7), Р. 1224–1238. DOI: https://doi.org/10.1109/tkde.2018.2827988
Shubin, I., Kozyriev, A., Liashik, V., Chetverykov, G. (2021), "Methods of adaptive knowledge testing based on the theory of logical networks". CEUR Workshop Proceedings. CEUR-WS. Р. 1184–1193, available at: https://ceur-ws.org/Vol-2870/paper86.pdf (last accessed 12.03.2024).
Harrington, J.L. (2016), "Relational Database Design and Implementation: Fourth Edition". Elsevier Inc. 689 p. DOI: https://doi.org/10.1016/C2015-0-01537-4
Meijer, E., Bierman, G. (2011), "A co-relational model of data for large shared data banks". Communications of the ACM. No. 54(4), Р. 49–58. DOI: https://doi.org/10.1145/1924421.1924436
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