@article{XU2026, 
author = {Cong XU and Mengxin SHI and Hongfeng WANG and Qingyu JIA and Jia ZHI and Jiasen YANG},
title = {Prediction method for matching between in-orbit satellites and satellite network filings based on knowledge graph},
year = {2026},
journal = {Journal of Beijing University of Aeronautics and Astronautics},
volume = {52},
number = {6},
pages = {1944-1954},
keywords = {matching prediction, knowledge graph, knowledge fusion, unsupervised learning, graph partition, in-orbit satellites, satellite network filings},
url = {https://www.sciopen.com/article/10.13700/j.bh.1001-5965.2024.0217},
doi = {10.13700/j.bh.1001-5965.2024.0217},
abstract = {Matching in-orbit satellites with International Telecommunication Union (ITU) satellite network declaration filings is crucial for the design, selection, declaration, and coordination of satellite frequencies and orbits. Due of their low matching efficiency and high domain knowledge requirements, traditional manual matching algorithms frequently encounter difficulties. To address these issues, we propose an unsupervised prediction of matching between in-orbit satellites and satellite network filings (UPMIS) method. This method establishes a prediction indicator system and knowledge graphs for both in-orbit satellites and satellite network filings. By integrating domain knowledge and graph partitioning, we design a three-tier filtering framework comprising a time parameter module, a numerical orbit parameter module, and a character-based societal parameter module. This framework enables fast and accurate matching between in-orbit satellites and satellite network filings. Experimental results demonstrate that UPMIS achieves a H10 score of 0.8542 on real datasets, outperforming other comparative models. Additionally, the average runtime reaches millisecond-level efficiency. Additionally, the trials offer helpful references for future matching relationship predictions by recommending values for parameters like filtering quantity and aggregation depth.}
}