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Hierarchical multi-label classification model for science and technology news based on heterogeneous graph semantic
Quan Cheng1,2, Jingyi Cheng1, Jian Chen2,3
1School of Economics and Management, Fuzhou University, Fuzhou, Fujian, China.
Peerj. Computer Science
|December 9, 2024
Summary
This study introduces a new hierarchical multi-label classification model for science and technology news. It effectively addresses challenges in classifying complex news by integrating heterogeneous graph semantics and domain knowledge, improving accuracy.
Area of Science:
- Information Science
- Computer Science
- Technology Management
Background:
- Online science and technology news is crucial for disseminating advancements but suffers from disordered, multi-dimensional data.
- Existing single-label classification methods struggle with the hierarchical nature and complex semantics of this news.
- There is a need for advanced models to effectively categorize and understand the vast amount of science and technology information.
Discussion:
- A novel hierarchical multi-label classification model is proposed, leveraging heterogeneous graph semantics.
- The model utilizes a hierarchical transmission module to capture thematic and structural features.
- Graph convolutional networks and domain knowledge graphs are integrated to enhance semantic understanding and address data scarcity.
Key Insights:
- The proposed model significantly outperforms baseline methods in classifying science and technology news.
- Achieved precision, recall, and F1 scores of 84.21%, 88.89%, and 86.49%, respectively.
- Demonstrates the effectiveness of heterogeneous graph semantics in handling complex, hierarchical data.
Outlook:
- This research offers a robust solution for hierarchical multi-label classification tasks.
- The model shows significant potential for applications facing data scarcity and intricate thematic classification.
- Future work could explore further refinements in graph representation and knowledge integration for enhanced news analysis.
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