面向地质大数据的语义检索模型研究
发布时间：2018-08-07 10:00
【摘要】：随着物联网、互联网、大数据、云计算等新一代信息技术的发展和在地学领域的应用,如何更全、更准的从海量地质数据中检索到用户所需要的信息,并以一种智能的方式推荐给用户,成了亟待解决的问题。本文从地质大数据语义模型定义和基于知识图谱技术的地质大数据语义模型构建入手,提出了一套面向地质大数据的语义检索和聚类分析方法,并完成了面向地质大数据语义检索系统平台的开发、实施及实验验证。验证结果表明此方法在查准率和查全率方面较其他方法更具优越性。
[Abstract]:With the development of the Internet of things, big data, cloud computing and other new generation of information technology and applications in the field of geoscience, how to retrieve the information that users need from the massive geological data more completely and accurately, And an intelligent way to recommend to the user, has become an urgent problem. Based on the definition of geological big data semantic model and the construction of geological big data semantic model based on knowledge map technology, a set of semantic retrieval and clustering analysis methods for geological big data are proposed in this paper. The development, implementation and experimental verification of the semantic retrieval platform for geological big data are completed. The results show that this method is superior to other methods in precision and recall.
【作者单位】： 中国地质调查局发展研究中心;国土资源部地质信息技术重点实验室;中国地质大学(北京)地球科学与资源学院;北京久远太和科技有限公司;湖南科技大学地理空间信息技术国家地方联合工程实验室;
【基金】：国土资源部公益性行业科研专项资助(编号:201511079)
【分类号】：P628
本文编号：2169672
[Abstract]:With the development of the Internet of things, big data, cloud computing and other new generation of information technology and applications in the field of geoscience, how to retrieve the information that users need from the massive geological data more completely and accurately, And an intelligent way to recommend to the user, has become an urgent problem. Based on the definition of geological big data semantic model and the construction of geological big data semantic model based on knowledge map technology, a set of semantic retrieval and clustering analysis methods for geological big data are proposed in this paper. The development, implementation and experimental verification of the semantic retrieval platform for geological big data are completed. The results show that this method is superior to other methods in precision and recall.
【作者单位】： 中国地质调查局发展研究中心;国土资源部地质信息技术重点实验室;中国地质大学(北京)地球科学与资源学院;北京久远太和科技有限公司;湖南科技大学地理空间信息技术国家地方联合工程实验室;
【基金】：国土资源部公益性行业科研专项资助(编号:201511079)
【分类号】：P628
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