| Autor: |
Thi Thanh Sang Nguyen, Pham Minh Thu Do, Thanh Tuan Nguyen, Thanh Tho Quan |
| Jazyk: |
angličtina |
| Rok vydání: |
2023 |
| Předmět: |
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| Zdroj: |
EAI Endorsed Transactions on Industrial Networks and Intelligent Systems, Vol 10, Iss 2 (2023) |
| Druh dokumentu: |
article |
| ISSN: |
2410-0218 |
| DOI: |
10.4108/eetinis.v10i2.2726 |
| Popis: |
Transforming data into appropriate formats is crucial because it can speed up the training process and enhance the performance of classification algorithms. It is, however, challenging due to the complicated process, resource-intensive and preserved meaning of the data. This study proposes new approaches to building knowledge representation models using word-embedding and ontology techniques, which can transform text data into digital data and still keep semantic/context information of themselves in order to enhance modeling data later. To evaluate the effectiveness of the built models, a classification framework is proposed and performed on a public real dataset. Experimental results show that the constructed knowledge representation models contribute significantly to the performance of classification methods. |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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