Node importance evaluation model for educational knowledge graph based on topological structure and similarity information fusion
Educational knowledge graph is an important tool for representing relationships between knowledge and concept in the field of education. Understanding and evaluating the importance of node in educational knowledge graph are crucial for tasks like educational resource management and learning path rec...
Saved in:
| Main Authors: | , , , , |
|---|---|
| Format: | Article |
| Language: | zho |
| Published: |
China InfoCom Media Group
2025-01-01
|
| Series: | 大数据 |
| Subjects: | |
| Online Access: | http://www.j-bigdataresearch.com.cn/zh/article/111999460/ |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| Summary: | Educational knowledge graph is an important tool for representing relationships between knowledge and concept in the field of education. Understanding and evaluating the importance of node in educational knowledge graph are crucial for tasks like educational resource management and learning path recommendation. However, traditional node importance evaluation methods often treat all nodes equally, considering only certain topological structures, and fail to capture the comprehensive characteristics of node. To address this, we proposed TSFM driven by key node. TSFM evaluated node importance by integrating topological structure with semantic and structural similarity information driven by key node. Specifically, TSFM utilized graph neural networks to embed nodes in knowledge graph, and optimized the embedding representation by considering topological and semantic similarity between nodes. Experimental results demonstrate that TSFM outperforms traditional algorithms and recent graph neural network models across multiple evaluation metrics. |
|---|---|
| ISSN: | 2096-0271 |