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GEOGRAPHIC KNOWLEDGE BASE DESIGN AND IMPLEMENTATION (ARTIFICIAL INTELLIGENCE, GEOGRAPHIC INFORMATION SYSTEMS, SPATIAL DATABASES, REPRESENTATION, MACHINE LEARNING).

机译:地理知识基础的设计和实现(人工智能,地理信息系统,空间数据库,表示,机器学习)。

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摘要

The absence of intelligent search and learning capabilities is a prime weakness in current Geographic Information Systems (GIS). The resulting rigidity of their operation severely limits their use. In response to this problem, research and development of a KBGIS (Knowledge Based Geographic Information System) based on Artificial Intelligence (AI) techniques was carried out by a team of researchers at The University of California at Santa Barbara. The objective of the KBGIS system was to respond intelligently to user queries on large spatial databases. The spatial database included topographic data, remote sensing data and thematic maps which were implemented using the Quad(,-)tree data structure.;The design and implementation of most of KBGIS's components is described. Dual spatial and propositional data structures were used for representing the spatial knowledge. The principle of representational duality runs as a major design thread at the macro (KB-GIS), meso (KB) and micro (spatial object) structural levels.;The design was guided by the hypothesis that machine spatial cognition entails a shift in importance from low-level spatial representation to high-level symbolic representation. Implemented data structures included the following knowledge representations: logic (predicate calculus), AND/OR trees, frames and discrimination nets. Procedures included: initialization, user interface, query parser, rote learning and parts of location learning and example learning.;The most apparent success in terms of performance turned out to be the location learning process and its spatial inheritance mechanism. It was found that the dynamic location learning capability, in conjunction with a likely user querying behavior, results in progressively diminishing search times for related queries. The extremely important example learning process was found to be too crude and very slow. The failure to provide adequate handling of spatial relations within and between objects is viewed as the main shortcoming. It was concluded that the basic KBGIS architecture can serve as a prototype for future generations of knowledge based geographic querying systems. The theoretical ramifications of several general principles involving spatial knowledge which have emerged from this work, transcend beyond the relative narrow scope of the system described.
机译:缺乏智能的搜索和学习功能是当前地理信息系统(GIS)的主要弱点。其操作的刚性导致严重限制了其使用。针对这一问题,加利福尼亚大学圣塔芭芭拉分校的一组研究人员基于人工智能(AI)技术对KBGIS(基于知识的地理信息系统)进行了研究和开发。 KBGIS系统的目标是智能地响应大型空间数据库上的用户查询。空间数据库包括地形数据,遥感数据和专题图,它们是使用Quad(,-)树数据结构实现的。;描述了KBGIS大部分组件的设计和实现。空间和命题的双重数据结构用于表示空间知识。表示对偶性的原理在宏(KB-GIS),中观(KB)和微观(空间对象)结构级别上作为主要设计线程运行。;该设计基于以下假设:机器空间认知意味着重要性发生了变化。从低级空间表示到高级符号表示。已实现的数据结构包括以下知识表示:逻辑(谓词演算),AND / OR树,框架和判别网。过程包括:初始化,用户界面,查询解析器,死记硬背的学习以及部分位置学习和示例学习。在性能方面,最明显的成功是位置学习过程及其空间继承机制。已经发现,动态位置学习能力与可能的用户查询行为相结合,导致相关查询的搜索时间逐渐减少。发现极其重要的示例学习过程过于粗糙且非常缓慢。主要缺点是未能提供足够的空间来处理对象内部和对象之间的空间关系。结论是,基本的KBGIS体系结构可以用作下一代基于知识的地理查询系统的原型。从这项工作中出现的涉及空间知识的几条通用原理的理论分支超出了所描述系统的相对狭窄范围。

著录项

  • 作者

    PAZNER, MICHA ITZHAK.;

  • 作者单位

    University of California, Santa Barbara.;

  • 授予单位 University of California, Santa Barbara.;
  • 学科 Geography.
  • 学位 Ph.D.
  • 年度 1986
  • 页码 183 p.
  • 总页数 183
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类 自然地理学;
  • 关键词

  • 入库时间 2022-08-17 11:51:05

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