地理数据空间本体构建及其在数据检索中的应用

随着新地理信息时代的来临,地理数据已经呈现出爆炸式增长的趋势。如何在海量的地理数据中准确、及时地找到人们所需要的数据,并把相关联的数据智能地推荐给用户,成为亟待解决的一大难题。针对传统以关键词、主题词等字符串匹配为核心的数据发现方法存在的查不全、查不准的问题,本文通过对地理空间中的概念、属性、关系、规则,以及相应实例的详细表达,初步提出了地理空间本体构建框架,并在此基础上构建了较为完整的地理数据空间本体,以实现地理数据的智能关联,最后在地球系统科学数据共享平台中进行应用实践。结果表明,引入地理数据空间本体后,检索的结果在数据的查全和查准方面显著提高,而且还能智能推荐相关联的数据信息。本文构建的地理数据空间本体对于大数据时代背景下地理数据的精确发现和共享有重要意义。

Construction of Geodata Spatial Ontology and Its Application in Data Retrieval

Geodata has been increasing at a tremendous speed in what we call the new era of Geographic Information. It is a challenge to accurately find the needed data in a real-time fashion amongst the massive spatial data and to recommend the associated required data intelligently to users. In light of the problem that the data retrieval by traditional searching methods based on keywords and topic words cannot satisfy the real needs emerged in the geoscience research, this study built a Geodata Spatial-Ontology Model through a detailed expression of the concepts, characteristics, relationship and instances of spatial data. In addition, this article presents the qualitative and quantitative spatial relationship, which plays an inestimable role in describing the inner connection between spatial objects. Then a Geodata Spatial-Ontology is built according to the Geodata Spatial-Ontology Model, and most of the concepts, instances and the relationship between them are elaborated. At the end of this paper, an application has been developed to parse the ontology and extract the semantic information using Apache Jena, which is a free and open source Java framework designed to build the Semantic Web and linked data applications. By associating the Geodata Spatial-Ontology with the Data Sharing Infrastructure of Earth System Science, the experiment in this research proves that the proposed ontology shows a significant improvement in data discovery. The Geodata Spatial Ontology constructed in this paper will have an important significance to the accurate data discovery and data sharing in the era of big data.