Using Transformer Models for Knowledge Graph Construction in Computer Science Education
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Abstract
The volume of information that can be used in the development of knowledge bases that can be used in education is constantly increasing. Also, this amount of data is very difficult to process and store. When designing a knowledge base to optimize the educational process, it is important to use ontologies. At the moment, the creation of an ontological knowledge model is the most promising option for storing and processing information. The article describes effective approaches for generating an ontological model using machine learning models based on the Transformer model.
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References
[1]
Anton Anikin, Marina Kultsova, Irina Zhukova, Natalia Sadovnikova, and Dmitry Litovkin. 2014. Knowledge Based Models and Software Tools for Learning Management in Open Learning Network. In CCIS. Springer International Publishing, 156--171. https://doi.org/10.1007/978-3-319-11854-3_15
[2]
Lillian N. Cassel, Robert H. Sloan, Gordon Davies, Heikki Topi, and Andrew McGettrick. 2007. The Computing Ontology Project: The Computing Education Application. SIGCSE Bull., Vol. 39, 1 (mar 2007), 519--520. https://doi.org/10.1145/1227504.1227486
[3]
Shubham Chatterjee and Laura Dietz. 2022. BERT-ER: Query-Specific BERT Entity Representations for Entity Ranking. In Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval (Madrid, Spain) (SIGIR '22). Association for Computing Machinery, New York, NY, USA, 1466--1477. https://doi.org/10.1145/3477495.3531944
[4]
VA Ivanin, IM Smurov, VV Ivanov, TV Batura, VV Sarkisyan, EL Artemova, and EV Tutubalina. 2020. Rurebus-2020 shared task: Russian relation extraction for business. (2020). io
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Published In
March 2023
1481 pages
ISBN:9781450394338
DOI:10.1145/3545947
- General Chairs:
- Maureen Doyle,
- Ben Stephenson,
- Program Chairs:
- Brian Dorn,
- Leen-Kiat Soh,
- Lina Battestilli
Copyright © 2022 Owner/Author.
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Published: 06 March 2023
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SIGCSE 2023
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SIGCSE 2023: The 54th ACM Technical Symposium on Computer Science Education
March 15 - 18, 2023
Toronto ON, Canada
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