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Enhancing computer-aided translation system with BiLSTM and convolutional neural network using a knowledge graph approach

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Abstract

This research aims to develop platform integration, service discovery, and recommendation system within the Internet of Things (IoT) industry. It accomplishes this endeavor through the integration of improved Long Short-Term Memory (LSTM) and Support Vector Machine (SVM) techniques to construct a Capability Knowledge Graph (CKG) for the IoT and a computer-aided translation (CAT) system. Initially, the distinctive characteristics of IoT are analyzed, leading to the proposition of a Bidirectional Long Short-Term Memory (BiLSTM) model designed to extract specific vocabulary from textual corpora. Subsequently, the BiLSTM-Convolutional Neural Networks (BiLSTM-CNN) model is introduced. Finally, extensive experiments are conducted to evaluate the effectiveness of the proposed model. The results demonstrate the superior performance of the model in entity labeling within sentences. Specifically, it achieves an accuracy of 85.1% for the top 100 entities and 80.0% for the top 200 entities. Additionally, this model surpasses current state-of-the-art methods in relation extraction, attaining the highest extraction results on the dataset. The constructed translation system exhibits an accuracy of 60.22% and a remarkable recall rate of 59.64%. The proposed system significantly contributes valuable data support for the development of IoT CKGs and CAT systems.

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Data availability

The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.

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Acknowledgements

The authors acknowledge the help from the university colleagues.

Funding

This research received no external funding.

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Authors and Affiliations

  1. School of Foreign Languages and Cultures, Xichang University, Xichang, 615000, China

    Yueting Xiang

  2. Tianyuan College, Hangzhou, China

    Yangfen Chen

  3. The Affiliated School of the Future Science and Technology City to Hangzhou Normal University, Hangzhou, China

    Yangfen Chen

  4. Department of Foreign Languages, Nanchong Vocational and Technical College, Nanchong, China

    Wenjuan Fan

  5. School of Education, Taylor’s University, No. 1 Jalan Taylor’s, Subang Jaya, Selangor, Malaysia

    Hui Ye

Authors
  1. Yueting Xiang
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  2. Yangfen Chen
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  3. Wenjuan Fan
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  4. Hui Ye
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Contributions

YX contributed to conceptualization, methodology, and writing—original draft preparation. YC contributed to data curation and visualization; WF contributed to investigation and visualization; HY contributed to formal analysis, writing—review and editing. All authors listed have made a substantial, direct and intellectual contribution to the work, and approved it for publication.

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Correspondence to Hui Ye.

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Xiang, Y., Chen, Y., Fan, W. et al. Enhancing computer-aided translation system with BiLSTM and convolutional neural network using a knowledge graph approach. J Supercomput 80, 5847–5869 (2024). https://doi.org/10.1007/s11227-023-05686-2

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  • DOI: https://doi.org/10.1007/s11227-023-05686-2

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