Spatio-Temporal Relevance Classification from Geographic Texts Using Deep Learning

Author:

Tian Miao1,Hu Xinxin2,Huang Jiakai3,Ma Kai2,Li Haiyan2,Zheng Shuai2,Tao Liufeng45,Qiu Qinjun45

Affiliation:

1. Key Laboratory of Geological Survey and Evaluation of Ministry of Education, China University of Geosciences, Wuhan 430074, China

2. College of Computer and Information Technology, China Three Gorges University, Yichang 443002, China

3. Hubei Geological Survey, Wuhan 430034, China

4. School of Computer Science, China University of Geosciences, Wuhan 430074, China

5. Ministry of Nature Resources Key Laboratory of Quantitative Resources Assessment and Information Technology, Wuhan 430074, China

Abstract

The growing proliferation of geographic information presents a substantial challenge to the traditional framework of a geographic information analysis and service. The dynamic integration and representation of geographic knowledge, such as triples, with spatio-temporal information play a crucial role in constructing a comprehensive spatio-temporal knowledge graph and facilitating the effective utilization of spatio-temporal big data for knowledge-driven service applications. The existing knowledge graph (or geographic knowledge graph) takes spatio-temporal as the attribute of entity, ignoring the role of spatio-temporal information for accurate retrieval of entity objects and adaptive expression of entity objects. This study approaches the correlation between geographic knowledge and spatio-temporal information as a text classification problem, with the aim of addressing the challenge of establishing meaningful connections among spatio-temporal data using advanced deep learning techniques. Specifically, we leverage Wikipedia as a valuable data source for collecting and filtering geographic texts. The Open Information Extraction (OpenIE) tool is employed to extract triples from each sentence, followed by manual annotation of the sentences’ spatio-temporal relevance. This process leads to the formation of quadruples (time relevance/space relevance) or quintuples (spatio-temporal relevance). Subsequently, a comprehensive spatio-temporal classification dataset is constructed for experiment verification. Ten prominent deep learning text classification models are then utilized to conduct experiments covering various aspects of time, space, and spatio-temporal relationships. The experimental results demonstrate that the Bidirectional Encoder Representations from Transformer-Region-based Convolutional Neural Network (BERT-RCNN) model exhibits the highest performance among the evaluated models. Overall, this study establishes a foundation for future knowledge extraction endeavors.

Funder

National Key R&D Program of China

Natural Science Foundation of China

Natural Science Foundation of Hubei Province of China

Open Fund of Hubei Key Laboratory of Intelligent Vision Based Monitoring for Hydroelectric Engineering

Open Fund of Key Laboratory of Urban Land Resources Monitoring and Simulation, Ministry of Natural Resources

China Postdoctoral Science Foundation

Publisher

MDPI AG

Subject

Earth and Planetary Sciences (miscellaneous),Computers in Earth Sciences,Geography, Planning and Development

Reference53 articles.

1. Some thoughts on basic surveying and mapping production service system under the new system;Ruan;Surv. Mapp. Sci.,2020

2. Basic issues and research agenda of geospatial knowledge service;Chen;Geomat. Inf. Sci. Wuhan Univ.,2019

3. Rethinking ubiquitous mapping in the age of intelligence;Liu;J. Surv. Mapp.,2020

4. Spatio-temporal features based geographical knowledge graph construction;Zhang;Sci. Sin. Inform.,2020

5. Spatio-temporal knowledge graph: Advances and perspectives;Lu;J. Geo-Inf. Sci.,2023

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