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An adaptive representation model for geoscience knowledge graphs considering complex spatiotemporal features and relationships

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Science China Earth Sciences 2023年第11期66卷 2563-2578页

作者： Yunqiang ZHU Kai SUN Shu WANG Chenghu ZHOU Feng LU Hairong LV Qinjun QIU Xinbing WANG Yanmin QI State Key Laboratory of Resources and Environmental Information System Institute of Geographic Sciences and Natural Resources ResearchChinese Academy of SciencesBeijing 100101China Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application Nanjing 210023China Department of Automation Tsinghua UniversityBeijing 100084China School of Computer Science China University of GeosciencesWuhan 430074China Department of Electronic Engineering Shanghai Jiao Tong UniversityShanghai 200240China School of Computer Science University of Nottingham Ningbo ChinaNingbo 315100China

Geoscience knowledge graph(GKG)can organize various geoscience knowledge into a machine understandable and computable semantic network and is an effective way to organize geoscience knowledge and provide knowledge-related *** a result,it has gained significant attention and become a frontier in *** knowledge is derived from many disciplines and has complex spatiotemporal features and relationships of multiple scales,granularities,and ***,establishing a GKG representation model conforming to the characteristics of geoscience knowledge is the basis and premise for the construction and application of ***,existing knowledge graph representation models leverage fixed tuples that are limited in fully representing complex spatiotemporal features and *** address this issue,this paper first systematically analyzes the categorization and spatiotemporal features and relationships of geoscience *** this basis,an adaptive representation model for GKG is proposed by considering the complex spatiotemporal features and *** the constraint of a unified spatiotemporal ontology,this model adopts different tuples to adaptively represent different types of geoscience knowledge according to their spatiotemporal *** model can efficiently represent geoscience knowledge,thereby avoiding the isolation of the spatiotemporal feature representation and improving the accuracy and efficiency of geoscience knowledge *** can further enable the alignment,transformation,computation,and reasoning of spatiotemporal information through a spatiotemporal ontology.

关键词： Geoscience Knowledge graph representation model Spatiotemporal features Spatiotemporal relationships

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Achieving High Navigation Accuracy for Spacecraft Using High-Order representation model of Disturbing Gravity

Achieving High Navigation Accuracy for Spacecraft Using High...

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第37届中国控制会议

作者： Lei Wang Wei Zheng Xiang Zhou College of Aerospace Science and Engineering National University of Defense Technology

Aiming at the low precision of gravity model in the Inertial Navigation System（INS） of the vertical launch vehicle, a disturbing gravity representation model which covers the launch area was presented in this paper. Even trading accuracy against speed, the results achieved by this method have sufficient precision for navigation calculation. By this means, the disturbing gravity is assigned to the navigation computation loop within a very short time and with an extraordinary accuracy. Several numerical simulations have shown that the average approximation error of disturbing gravity is less than 2 mGal at any height level and the relative approximation error is less 2%.

关键词： Inertial Navigation System vertical launch vehicle disturbing gravity representation model approximation error

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Developing a geographic Case-Based Reasoning approach

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Journal of Environmental Science and Engineering 2007年第1期1卷 1-7,18页

作者： DU Yun-yan ZHOU Cheng-hu SU Fen-zhen SHI Wen-zhong The State Key Lab of Environment and Information System Institute of Geography Environment and Resources CAS Beijing 100101 China Advanced Research Centre for Spatial Information Technology Department of Land-Surveying and Geo-lnformatics the Hong Kong Polytechnic University

Case-Based Reasoning （CBR） is an AI approach and been applied to many areas. However, one area - geography - has not been investigated systematically and thus has been identified as the focus for this study. This paper intends to further extend current CBR to a geographic CBR （Geo-CBR）. First, the concept of Geo-CBR is proposed. Second, a representation model for geographic cases has been established based on the Tesseral model and on a further extension in spatio-temporal dimensions for geographic cases. Third, a reasoning model for Geo-CBR is developed by considering the spatio-temporat characteristics and the uncertain and limited information of geographic cases. Finally, the Geo-CBR model is applied to forecasting the production of ocean fisheries to demonstrate the applicability of the developed Geo-CBR in solving problems in the real world. According to the experimental results, Geo-CBR is an effective and easy-to-implement approach for predicting geographic cases quantitatively.

关键词： Case-Based Reasoning (CBR) geographic CBR (Geo-CBR) representation model reasoning model Tesseral model

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