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...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | zho |
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China InfoCom Media Group
2025-01-01
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| Series: | 大数据 |
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| Online Access: | http://www.j-bigdataresearch.com.cn/zh/article/111999460/ |
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| author | LI Meizi WU Yunfang LU Shuyi Wang Hao YANG Ru |
| author_facet | LI Meizi WU Yunfang LU Shuyi Wang Hao YANG Ru |
| author_sort | LI Meizi |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-3ea7d213c94e418bb7948a1a878f4250 |
| institution | Kabale University |
| issn | 2096-0271 |
| language | zho |
| publishDate | 2025-01-01 |
| publisher | China InfoCom Media Group |
| record_format | Article |
| series | 大数据 |
| spelling | doaj-art-3ea7d213c94e418bb7948a1a878f42502025-08-20T03:32:12ZzhoChina InfoCom Media Group大数据2096-02712025-01-01120111999460Node importance evaluation model for educational knowledge graph based on topological structure and similarity information fusionLI MeiziWU YunfangLU ShuyiWang HaoYANG RuEducational 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.http://www.j-bigdataresearch.com.cn/zh/article/111999460/Educational Knowledge GraphNode Importance Evaluationtopological structuregraph attention network |
| spellingShingle | LI Meizi WU Yunfang LU Shuyi Wang Hao YANG Ru Node importance evaluation model for educational knowledge graph based on topological structure and similarity information fusion 大数据 Educational Knowledge Graph Node Importance Evaluation topological structure graph attention network |
| title | Node importance evaluation model for educational knowledge graph based on topological structure and similarity information fusion |
| title_full | Node importance evaluation model for educational knowledge graph based on topological structure and similarity information fusion |
| title_fullStr | Node importance evaluation model for educational knowledge graph based on topological structure and similarity information fusion |
| title_full_unstemmed | Node importance evaluation model for educational knowledge graph based on topological structure and similarity information fusion |
| title_short | Node importance evaluation model for educational knowledge graph based on topological structure and similarity information fusion |
| title_sort | node importance evaluation model for educational knowledge graph based on topological structure and similarity information fusion |
| topic | Educational Knowledge Graph Node Importance Evaluation topological structure graph attention network |
| url | http://www.j-bigdataresearch.com.cn/zh/article/111999460/ |
| work_keys_str_mv | AT limeizi nodeimportanceevaluationmodelforeducationalknowledgegraphbasedontopologicalstructureandsimilarityinformationfusion AT wuyunfang nodeimportanceevaluationmodelforeducationalknowledgegraphbasedontopologicalstructureandsimilarityinformationfusion AT lushuyi nodeimportanceevaluationmodelforeducationalknowledgegraphbasedontopologicalstructureandsimilarityinformationfusion AT wanghao nodeimportanceevaluationmodelforeducationalknowledgegraphbasedontopologicalstructureandsimilarityinformationfusion AT yangru nodeimportanceevaluationmodelforeducationalknowledgegraphbasedontopologicalstructureandsimilarityinformationfusion |