文本中空间信息的结构化建模与语义定位

doi:10.11947/j.AGCS.2023.20220066

测绘学报 ›› 2023, Vol. 52 ›› Issue (8): 1398-1410.doi: 10.11947/j.AGCS.2023.20220066

文本中空间信息的结构化建模与语义定位

王大力¹, 童晓冲^1,2, 孟丽³, 雷毅¹, 郭从洲¹, 张有为²

1. 信息工程大学地理空间信息学院, 河南郑州 450001;
2. 信息工程大学先进技术研究院, 河南郑州 450001;
3. 31016部队, 北京 100081

收稿日期:2022-01-26 修回日期:2022-10-10 发布日期:2023-09-07
通讯作者: 童晓冲 E-mail:txchr@zxiat.org
作者简介:王大力(1999-),男,硕士生,研究方向为时空语义和网格时空数据库。E-mail:wangdlemail@163.com
基金资助:
国家重点研发计划(2018YFB0505304);河南省自然科学基金优秀青年基金(212300410096);嵩山实验室项目(纳入河南省重大科技专项管理体系)(221100211000-03)

Structural modeling of spatial information in texts and semantic localization

WANG Dali¹, TONG Xiaochong^1,2, MENG Li³, LEI Yi¹, GUO Congzhou¹, ZHANG Youwei²

1. Institute of Geospatial Information, Information Engineering University, Zhengzhou 450001, China;
2. Zhengzhou Xinda Institute of Advanced Technology, Zhengzhou 450001, China;
3. Troops 31016, Beijing 100081, China

Received:2022-01-26 Revised:2022-10-10 Published:2023-09-07
Supported by:
The National Key Research and Development Plan of China (No. 2018YFB0505304); The Excellent Youth Foundation of Henan Municipal Natural Science Foundation (No. 212300410096); Program of Song Shan Laboratory (Included in the Management of Major Science and Tech-nology Program of Henan Province) (No. 221100211000-03)

摘要/Abstract

摘要： 互联网上存在大量包含空间信息的文本,本文针对此类文本中描述事件位置的空间语义建模方法不统一、模糊处理方法不恰当等问题,利用方形离散格网建立结构化语义表达模型,使用统一的形式对方向、距离、拓扑3类基本语义及其组合形成的复杂语义进行表达,并使用卷积方法定量化空间语义中的模糊概念,将不确定的语义描述投影到地理空间,最终通过多句空间语义确定事件发生的地理位置。试验表明:①结构化语义表达模型能适用于包含多种空间关系的语义,在多语义联合建模求并时能确定未知事件发生的地理范围;②语义定位的可信度与语义类型、参考实体类型、参考实体数量及正确语义占比等因素有关,当参考实体数量较多时,能在正确语义数量小于错误语义数量的情况下确定事件发生的地理位置范围。

关键词: 空间信息建模, 语义建模, 语义定位, 方形离散格网

Abstract: A large number text including spatial information exist widely on the Internet. In order to solve the problems of inconsistent spatial semantic modeling methods and inappropriate fuzzy processing methods for describing the location of events in those text, this paper uses the square discrete grid to establish a structured semantic expression model, and uses a unified form to express three basic semantics (direction, distance and topology). The convolution method is used to quantify the fuzzy concepts in the spatial semantics, and the uncertain semantic description is projected to the geographical space, and finally the geographical location of the event is determined through the multi-sentence spatial semantics. Experiments show that: ① The structured semantic representation model can be applied to semantics with various types of spatial information, and can determine the geographic range of unknown events when multi-semantic joint modeling and merging; ② The credibility of semantic location is related to the semantic type, the type of reference entity, the number of reference entities, the proportion of correct semantics and other factors. When the number of reference entities is large, the geographical location range of events can be determined under the condition that the number of correct semantics is less than that of wrong.

Key words: spatial information modeling, semantic modeling, semantic localization, global discrete grid system

中图分类号:

P208

王大力, 童晓冲, 孟丽, 雷毅, 郭从洲, 张有为. 文本中空间信息的结构化建模与语义定位[J]. 测绘学报, 2023, 52(8): 1398-1410.

WANG Dali, TONG Xiaochong, MENG Li, LEI Yi, GUO Congzhou, ZHANG Youwei. Structural modeling of spatial information in texts and semantic localization[J]. Acta Geodaetica et Cartographica Sinica, 2023, 52(8): 1398-1410.

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文本中空间信息的结构化建模与语义定位

Structural modeling of spatial information in texts and semantic localization

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