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作 者:赵红伟[1,2,3] 诸云强[1,2] 侯志伟[1,2,3] 杨宏伟[4]
机构地区:[1]中国科学院资源与环境信息系统国家重点实验室,北京100101 [2]中国科学院地理科学与资源研究所,北京100101 [3]中国科学院大学,北京100049 [4]中国石油规划总院,北京100000
出 处:《地理科学》2016年第8期1180-1189,共10页Scientia Geographica Sinica
基 金:国家自然科学基金项目(41371381);科技部科技基础性工作专项项目(2013FY110900);国家重大科学仪器设备开发专项(2012YQ06002704);云南省科技计划项目(2012CA021)资助~~
摘 要:利用资源描述框架(RDF)设计地理空间元数据关联模型,根据地理空间元数据之间的语义关系和语义相关度的计算,以构建以元数据为节点、元数据之间的语义关系为边、语义相关度为权重的关联网络。在这一网络中,一个节点是一个地理空间元数据的资源描述图,包含属性特征(数据来源、空间特征、时间特征、内容)及其关系特征(元数据之间的语义关系、语义相关度)。实验及其分析表明,地理空间元数据关联网络可以有效地支持地理空间数据语义关联检索、推荐等应用,这与传统的基于关键词的元数据检索方式相比,具有更高的准确度。The rapid acquisition of geospatial data mainly depends on geospatial metadata. But the traditional organization of geospatial metadata and the keywords-based retrieval methods create barriers among metadata considering semantic relations between geospatial data such as spatial topology relationship, category relationship, resulting in a bottleneck in geospatial data sharing. In the context of big geospatial data, the development of linked data provides an effective practice for the semantic sharing and application of massive geospatial data. The linked geodata is intended to break the semantic barriers between geospatial data and form a data network with semantic realtions. Due to the complexity, diversity and uncertainy of geospatial data, linked geodata is often achieved through the association between metadata. Geospatial metadata contains a number of descriptive information. How to effectively organize vast amounts of geospatial metadata and map the metadata into the semantic space by simple way have become the hotspots in the field of geospatial data sharing. Construction of semantic associations among geospatial metadata is an effective means of performing semantic retrieval using related data technologies. Effective application of linked data depends on effective association models. Considering this, a method of constructing geospatial metadata association networks is proposed in this paper: firstly, a geospatial metadata association model is designed on basis of the resource description framework(RDF); secondly, a semantic relation between metadata is determined and the relationship is constructed; and finally, the degree of semantic relevance of the semantic relationship is calculated. In the association network, the metadata are nodes, the semantic relationships between the metadata are edges, and the degrees of semantic relevance are the weights of the edges. Every node is an RDF that has attribute properties,such as sources, spatial characteristics, temporal characteristics, and content, and ha
分 类 号:TP391[自动化与计算机技术—计算机应用技术]
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