曹启程, 朱欣焰, 吴瑞龙, 李铭. 基于本体的遥感数据需求文本时间获取及其语义计算[J]. 武汉大学学报 ( 信息科学版), 2021, 46(7): 1114-1122. DOI: 10.13203/j.whugis20190240
引用本文: 曹启程, 朱欣焰, 吴瑞龙, 李铭. 基于本体的遥感数据需求文本时间获取及其语义计算[J]. 武汉大学学报 ( 信息科学版), 2021, 46(7): 1114-1122. DOI: 10.13203/j.whugis20190240
CAO Qicheng, ZHU Xinyan, WU Ruilong, LI Ming. Time Extracting and Semantic Computing of Remote Sensing Data Demand Text Based on Ontology[J]. Geomatics and Information Science of Wuhan University, 2021, 46(7): 1114-1122. DOI: 10.13203/j.whugis20190240
Citation: CAO Qicheng, ZHU Xinyan, WU Ruilong, LI Ming. Time Extracting and Semantic Computing of Remote Sensing Data Demand Text Based on Ontology[J]. Geomatics and Information Science of Wuhan University, 2021, 46(7): 1114-1122. DOI: 10.13203/j.whugis20190240

基于本体的遥感数据需求文本时间获取及其语义计算

Time Extracting and Semantic Computing of Remote Sensing Data Demand Text Based on Ontology

  • 摘要: 从遥感数据需求文本中获取时间要素并进行语义计算是实现遥感数据需求文本可理解的关键步骤之一。通过对遥感数据需求文本时间要素及其构成形式的分析,建立了融合时间形式表达与语义计算一体的时间本体,并在此基础上给出了遥感数据需求文本时间信息抽取及其语义计算方法。实验表明,该方法对于遥感数据需求文本的时间要素具有较好的解析与推理效率,有助于提升自然语言获取遥感数据的效率。

     

    Abstract:
      Objectives  Remote sensing image demand text is a user-friendly approach for users to acquire images through natural language. Time is a key element when searching for remote sensing images, thus extracting and understanding time description in remote sensing image demand text is critical in-demand semantic analysis. However, a few pieces of research focus on time extraction and semantic computing of remote sensing image demand text, and time extraction and semantic computing are not combined to get full consideration of time semantic analysis.
      Methods  we collect remote sensing image demand texts from domains of disaster mitigation, agriculture, forestry, surveying and mapping. Then we extract time descriptions from demand texts manually and analyze their composition. By dividing time description into different time elements, a formal representation model is provided and time semantic representation rules as while as time semantic computation rules are proposed. Finally, a time ontology that integrates the time formal representation and semantic computing together is developed, which including four parts: Time semantic elements, time description, time model, and time knowledge. A rule-based time extracting method and a semantic computing method which both based on the time ontology are also given.
      Results  We carried out experiments on 2 000 remote sensing image demand texts, which were divided into five groups. The baseline was made manually and compared with the results of our methods. We got an average precision of 97.1%. Experiments show that our method has better efficiency for understanding time information in remote sensing data demand text.
      Conclusions  Integrating time extraction and semantic computing into one ontology providing a seamless comprehension of time description in remote sensing image demand text, which helps to improve the efficiency of obtaining remote sensing images through natural language.

     

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