测绘学报 ›› 2021, Vol. 50 ›› Issue (12): 1717-1727.doi: 10.11947/j.AGCS.2021.20200360

• 位置服务与地理空间信息处理 • 上一篇    下一篇

道路网选取的案例与本体推理方法

郭漩1, 钱海忠1, 王骁1, 刘俊楠2, 钟吉1   

  1. 1. 信息工程大学地理空间信息学院, 河南 郑州 450000;
    2. 信息工程大学数据目标与工程学院, 河南 郑州 450000
  • 收稿日期:2020-07-29 修回日期:2021-08-10 发布日期:2022-01-08
  • 通讯作者: 钱海忠 E-mail:haizhongqian@163.com
  • 作者简介:郭漩(1993—),女,博士生,研究方向为地图自动综合、空间数据挖掘。
  • 基金资助:
    国家自然科学基金(41571442);河南省杰出青年科学基金(212300410014);军队“双重”建设项目(f4203)

A method of road network selection based on case and ontology reasoning

GUO Xuan1, QIAN Haizhong1, WANG Xiao1, LIU Junnan2, ZHONG Ji1   

  1. 1. Institute of Geospatial Information, Information Engineering University, Zhengzhou 450000, China;
    2. Institute of Data and Target Engineering, Information Engineering University, Zhengzhou 450000, China
  • Received:2020-07-29 Revised:2021-08-10 Published:2022-01-08
  • Supported by:
    The National Natural Science Foundation of China (No. 41571442);The Excellent Youth Foundation of Henan Scientific Committee (No. 212300410014);"Dual "Construction Projects for the Military (No. f4203)

摘要: 道路网选取是一个模糊决策过程,针对当前制图综合案例推理算法中容易出现噪声和冲突、部分结果需人工参与决策等问题,本文以本体技术为基础,提出一种利用本体组织案例并进行知识推理的道路网选取方法。首先,从已有系列比例尺数据中提取案例,并基于三元法进行形式化描述;其次,以案例数据为中心构建本体知识库,重新组织案例表达方式;然后,利用本体规则扩展,识别并消除案例库中的噪声和冲突;最后,通过语义映射和图查询等推理手段,建立待选取数据语义、几何特征与本体知识库的映射关系,实现道路网自动选取。试验表明,该方法可有效还原专家知识,降低决策难度,促进道路网选取向知识推理方向发展。

关键词: 道路网选取, 案例, 本体, 知识推理

Abstract: The selection of road network is a fuzzy decision-making process. Aiming at problems such as noise, conflict and human decision-making in the cartographic generalization algorithm based on general case reasoning, this paper proposes a road network selection method which uses ontology to organize cases and conduct knowledge reasoning. In this paper, we extract expert cases from series scale map and describe them formally according to ternary model. To realize knowledge reasoning, these cases are used to build a domain ontology repository, reorganizing their representation format. Then, ontology rule extension helps to identify and eliminate noise and conflict cases. Additionally, with methods of semantic mapping and graph query, the semantic and geometric features of the road network to be selected are associated with the ontology repository to realize road network selection automatically.

Key words: road-network selection, case, ontology, knowledge reasoning

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