多源道路智能选取的本体知识推理方法

多源道路智能选取的本体知识推理方法

大数据时代道路数据来源日益增多，跨数据源的道路选取面临巨大挑战。本文针对数据语义不一致问题，提出一种基于本体知识推理的多源道路选取方法。首先，将1：5万基本比例尺地形图道路数据作为基础案例，将四维图新导航电子地图和开放街道地图中的道路数据作为试验数据，基于stroke计算道路等级、长度、连通度、接近度、中介度特征项，提取特征项概念并构建本体；然后，从语义特征项和数值特征项两方面计算本体概念相似性，建立基础案例与试验数据间的关联关系；最后，基于本体和语义网规则语言定义本体通用、语义特征、数值特征三类选取规则，实现跨数据源道路选取的过程性知识推理。试验表明，本文方法可基于本体概念相似性度量消除语义...

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Veröffentlicht in:Ce hui xue bao 2022-02, Vol.51 (2), p.279-289
Hauptverfasser: 郭漩, 钱海忠, 王骁, 刘俊楠, 任琰, 赵钰哲, 陈国庆
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Sprache:chi ; eng
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Big Data
Cartography
Experimental data
Mathematical analysis
Ontology
Reasoning
Roads
Semantic web
Semantics
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container_end_page 289
container_issue 2
container_start_page 279
container_title Ce hui xue bao
container_volume 51
creator 郭漩
钱海忠
王骁
刘俊楠
任琰
赵钰哲
陈国庆
description 大数据时代道路数据来源日益增多，跨数据源的道路选取面临巨大挑战。本文针对数据语义不一致问题，提出一种基于本体知识推理的多源道路选取方法。首先，将1：5万基本比例尺地形图道路数据作为基础案例，将四维图新导航电子地图和开放街道地图中的道路数据作为试验数据，基于stroke计算道路等级、长度、连通度、接近度、中介度特征项，提取特征项概念并构建本体；然后，从语义特征项和数值特征项两方面计算本体概念相似性，建立基础案例与试验数据间的关联关系；最后，基于本体和语义网规则语言定义本体通用、语义特征、数值特征三类选取规则，实现跨数据源道路选取的过程性知识推理。试验表明，本文方法可基于本体概念相似性度量消除语义差异，同时利用语义网规则语言进行知识推理，可实现多源道路数据向基本比例尺数据的智能选取。
doi_str_mv 10.11947/j.AGCS.2022.20210168
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subjects Big Data
Cartography
Experimental data
Mathematical analysis
Ontology
Reasoning
Roads
Semantic web
Semantics
title 多源道路智能选取的本体知识推理方法
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