| 引用本文: | 王 贺，孙国强，贾 琳，朱庆林. 一种面向电磁空间数据的语义组织方法[J]. 雷达科学与技术, 2026, 24(2): 185-195.[点击复制] |
| WANG He, SUN Guoqiang, JIA Lin, ZHU Qinglin. A Semantic Organization Method for Electromagnetic Space Data[J]. Radar Science and Technology, 2026, 24(2): 185-195.[点击复制] |
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| 摘要: |
| 针对网络电磁空间信号类数据语义获取复杂、难以深度开发与组织利用的问题，本文提出了一种新的语义组织方式，构建了一种基于特征和行为两级数据类型的图谱建模方法。首先，根据业务场景的需求，明确需要采集与处理的数据类型，并按照特征级与行为级分别完成结构化、非结构化的存储。然后，基于存储内容的关键特征词完成上下文关联数据的抽取，并采用ALBERT-BiLSTM-CRF模型识别数据中的关键实体和关系。最后，通过关联数据知识融合的方法来构建语义增强的知识图谱。该方法能够有效地解决电磁空间数据语义转化为清晰、准确的响应序列输出问题，并通过雷达应用场景分析了该方法的可行性。 |
| 关键词: 电磁空间数据 特征建模 语义解析 知识图谱 |
| DOI：DOI:10.3969/j.issn.1672-2337.2026.02.009 |
| 分类号:TN955 |
| 基金项目:乾元国家实验室基金项目（SKYZZF022025020013） |
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| A Semantic Organization Method for Electromagnetic Space Data |
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WANG He, SUN Guoqiang, JIA Lin, ZHU Qinglin
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1. China Academy of Electronics and Information Technology, Beijing 100041, China;2. The 38th Research Institute of China Electronics Technology Group Corporation, Hefei 230088, China;3. Hefei Institutes of Physical Science, Chinese Academy of Sciences, Hefei 230031, China;4. The 22nd Research Institute of China Electronics Technology Group Corporation, Qingdao 266107, China
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| Abstract: |
| Aimed at the challenges of complex semantic acquisition and difficulties in deep development and organized utilization of network electromagnetic space signal data, this paper proposes a new semantic organization approach. A graph-based modeling method utilizing feature-behavior two-level data typology is introduced. First, based on the requirements of the business scenario, the data types to be collected and processed are defined, with structured and unstructured storage implemented for feature-level and behavior-level data respectively. Then, the context-related data is extracted based on the key feature words of the stored content, and the ALBERT-BiLSTM-CRF model is used to identify the key entities and relationships in the data.Finally, a semantic enhanced knowledge graph is constructed through correlated data knowledge fusion. This method effectively resolves the problem of transforming electromagnetic space data semantics into clear and actionable response sequence outputs, and the feasibility of this method is analyzed through radar application scenarios. |
| Key words: electromagnetic space data feature modeling semantic parsing key word knowledge graph |
