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《地学前缘(英文版)》2023,14(2):101521
Lithofacies paleogeography is a data-intensive discipline that involves the interpretation and compilation of sedimentary facies. Traditional sedimentary facies analysis is a labor-intensive task with the added complexity of using unstructured knowledge and unstandardized terminology. Therefore, it is very difficult for beginners or non-geology scholars who lack a systematic knowledge and experience in sedimentary facies analysis. These hurdles could be partly alleviated by having a standardized, structured, and systematic knowledge base coupled with an efficient automatic machine-assisted sedimentary facies identification system. To this end, this study constructed a knowledge system for fluvial facies and carried out knowledge representation. Components include a domain knowledge graph for types of fluvial facies (meandering, braided and other fluvial depositional environments) and their characteristic features (bedforms, grain size distribution, etc.) with visualization, a method for query and retrieval on a graph database platform, a hierarchical knowledge tree-structure, a data-mining clustering algorithm for machine-analysis of publication texts, and an algorithm model for this area of sedimentary facies reasoning. The underlying sedimentary facies identification and knowledge reasoning system is based on expert experience and synthesis of publications. For testing, 17 sets literature publications data that included details of sedimentary facies data (bedforms, grain sizes, etc.) were submitted to the artificial intelligence model, then compared and validated. This testing set of automated reasoning results yielded an interpretation accuracy of about 90% relative to the published interpretations in those papers. Therefore, the model and algorithm provide an efficient and automated reasoning technology, which provides a new approach and route for the rapid and intelligent identification of other types of sedimentary facies from literature data or direct use in the field. 相似文献
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LBS中位置及其语义的研究 总被引：4，自引：0，他引：4
阐述了两种LBS位置表示方法，引入了语义位置的概念，对现有的语义位置概念作了修正。强调了位置属性也是语义位置的重要组成部分。通过对位置概念内涵和外延的分析，讨论了位置属性所包括的内容；运用层次建模，实现了位置之间关系的描述。为了能够规范化地描述语义位置，运用本体技术，对位置所包含的语义信息进行了描述，建立了能够被计算机识别和处理的OWI。位置本体模型。通过对语义位置的本体建模，实现了位置信息的知识化描述，为实现个性化服务奠定了基础。 相似文献
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基于形式化本体的基础地理信息分类 总被引：3，自引：0，他引：3
从本体概念出发,以认知分类和现有的本体分类方法为基础,将地理信息概念分为元概念和复合概念两大类,并基于一定的约束条件,根据地理信息概念的元属性,以形式化方法实现基础地理信息的分类. 相似文献
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基于Voronoi模型的海南岛旅游资源集合体空间边界提取 总被引：1，自引：0，他引：1
旅游资源是旅游业发展的物质条件,是开展各项旅游活动的载体和基础。旅游资源分类方法和评价理论研究已取得了较大的进展,但在旅游资源调查与规划实践中,以有研究通常将一个景区或大规模地理实体与小规模实体在同一个标准下衡量与对比,未考虑旅游资源的地理空间尺度特征。不同尺度的旅游地域空间,旅游资源评价、规划方法及其开发方向都不同。本文目的是通过梳理不同尺度旅游资源空间单元概念,对最难界定的集合体进行空间识别。基于集合体的概念认知,利用空间语义关系构建本体概念模型,提出了不同类型旅游资源集合体的空间边界提取方法。鉴于此,以海南岛为例进行实证研究,运用空间语义关系构建3种不同类型的旅游资源本体概念模型,在此基础上对不同类型旅游资源集合体进行条件约束判断,并利用泰森多边形与缓冲区分析方法对其进行空间识别。与规划范围结果对比发现,该方法可较好地近似表达旅游资源集合体空间边界及空间关系。每种类型的集合体空间语义关系存在树状层次结构,包含2个层次,空间形态呈多边形和带状分布。研究方法具有可操作性,能够为旅游规划与管理提供科学参考。 相似文献
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The quality and integrity of spatial data is very important to support interoperability among different systems. To reach this aim integrity rules defined by the application play an important role (for example, constraints between object classes). In this article, we propose a methodology to define integrity constraints using user level spatial relations between classes of individuals. We will also provide mapping rules from user level relations to geometric level operators to allow the computation of relations. As a case study, we will define the constraints for the class of rivers and some of its specializations. 相似文献
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We consider the problem of enabling interoperability and information sharing among geospatial applications that use ontologies to describe their concepts and the relationships among them. We present two fully automatic alignment methods that use the graph structures of a pair of ontologies to establish their alignment, that is, the semantic correspondences between their concepts. We have tested our methods on geospatial ontologies pertaining to wetlands and four other pairs that belong to a repository that has been used in the Ontology Alignment Evaluation Initiative (OAEI). Using these ontologies, we have compared the effectiveness (precision and recall) of our methods against the Similarity Flooding Algorithm that was proposed by others and show that for each of the tested ontologies one of our methods is at least as effective as their method. We have tuned the performance of our methods by introducing a greedy approach that reduces the number of concepts that get compared. This approach reduces runtime by approximately 30% with a minor compromise to the effectiveness of the results. To further validate our approach, we participated in the OAEI competition to align a pair of ontologies, each with a few thousand concepts. 相似文献
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