Machine learningNetwork science
加权知识图谱分析
加权知识图谱分析通过为实体间的边分配数值权重（例如置信度分数、共现频率或关系强度）来扩展标准的知识图谱方法。这些权重使分析人员能够优先处理高置信度三元组，找到最具影响力的路径，并在大型结构化知识库中计算考虑权重的中心性和社群结构。
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来源
- Hogan, A., Blomqvist, E., Cochez, M., d'Amato, C., Melo, G., Gutierrez, C., Kirrane, S., Gayo, J. E. L., Navigli, R., Neumaier, S., Ngomo, A. N., Polleres, A., Rashid, S. M., Rula, A., Schmelzeisen, L., Sequeda, J., Staab, S., & Zimmermann, A. (2021). Knowledge Graphs. ACM Computing Surveys, 54(4), 1–37. DOI: 10.1145/3447772 ↗
- Wang, Q., Zhang, F., Liu, Z., & Sun, M. (2017). Knowledge Graph Embedding by Translating on Hyperplanes. In Proceedings of the AAAI Conference on Artificial Intelligence, 28(1). link ↗
如何引用本页
ScholarGate. (2026, June 3). Weighted Knowledge Graph Analysis (Weight-Aware Structural and Semantic Network Analysis). ScholarGate. https://scholargate.app/zh/network-analysis/weighted-knowledge-graph-analysis
Which method?
Set this method beside its closest kin and read them side by side — the library lays the books on the table; the choice is yours.
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