Extracting geographic features from the Internet: A geographic information mining framework

Ying Zhang; Qunfei Ma; Yao Yi Chiang; Craig Knoblock; Xin Zhang; Puhai Yang; Minghe Gao; Xiang Hu

doi:10.1016/j.knosys.2019.02.031

Extracting geographic features from the Internet: A geographic information mining framework

Ying Zhang, Qunfei Ma, Yao Yi Chiang, Craig Knoblock, Xin Zhang, Puhai Yang, Minghe Gao, Xiang Hu

Research output: Contribution to journal › Article › peer-review

6 Scopus citations

Abstract

In this paper, we propose a Geographic Information Mining framework to contribute some exploratory results concerning harvesting the featured place information entities from the Web. In the framework, we suggest an iterative geographic information mining model reflecting the data evolution along the mining process. Associating the iterations, we propose a set of methodologies and integrate them into the processing onto solving the critical issues concerning collecting data, filtering irrelevant samples and extracting featured entities. According to the experiments, the contribution brings in a sound systematic solution to enrich the existing digital gazetteers as complete as Google Maps.

Original language	English (US)
Pages (from-to)	57-72
Number of pages	16
Journal	Knowledge-Based Systems
Volume	174
DOIs	https://doi.org/10.1016/j.knosys.2019.02.031
State	Published - Jun 15 2019
Externally published	Yes

Bibliographical note

Funding Information:
This work was supported in part by the Fundamental Research Funds for the Central Universities under Grant 2018MS024 in part by the National Natural Science Foundation of China under Grant 61305056 , and in part by the Overseas Expertise Introduction Program for Disciplines Innovation in Universities (Project 111) under Grant B13009 .

Publisher Copyright:
© 2019 Elsevier B.V.

Keywords

Geographic information mining
Geographic information mining framework
Place entity extraction
Place-name dataset

Publisher link

10.1016/j.knosys.2019.02.031

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abstract = "In this paper, we propose a Geographic Information Mining framework to contribute some exploratory results concerning harvesting the featured place information entities from the Web. In the framework, we suggest an iterative geographic information mining model reflecting the data evolution along the mining process. Associating the iterations, we propose a set of methodologies and integrate them into the processing onto solving the critical issues concerning collecting data, filtering irrelevant samples and extracting featured entities. According to the experiments, the contribution brings in a sound systematic solution to enrich the existing digital gazetteers as complete as Google Maps.",

keywords = "Geographic information mining, Geographic information mining framework, Place entity extraction, Place-name dataset",

author = "Ying Zhang and Qunfei Ma and Chiang, {Yao Yi} and Craig Knoblock and Xin Zhang and Puhai Yang and Minghe Gao and Xiang Hu",

note = "Funding Information: This work was supported in part by the Fundamental Research Funds for the Central Universities under Grant 2018MS024 in part by the National Natural Science Foundation of China under Grant 61305056 , and in part by the Overseas Expertise Introduction Program for Disciplines Innovation in Universities (Project 111) under Grant B13009 . Publisher Copyright: {\textcopyright} 2019 Elsevier B.V.",

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AU - Zhang, Ying

AU - Ma, Qunfei

AU - Chiang, Yao Yi

AU - Knoblock, Craig

AU - Zhang, Xin

AU - Yang, Puhai

AU - Gao, Minghe

AU - Hu, Xiang

N1 - Funding Information: This work was supported in part by the Fundamental Research Funds for the Central Universities under Grant 2018MS024 in part by the National Natural Science Foundation of China under Grant 61305056 , and in part by the Overseas Expertise Introduction Program for Disciplines Innovation in Universities (Project 111) under Grant B13009 . Publisher Copyright: © 2019 Elsevier B.V.

PY - 2019/6/15

Y1 - 2019/6/15

N2 - In this paper, we propose a Geographic Information Mining framework to contribute some exploratory results concerning harvesting the featured place information entities from the Web. In the framework, we suggest an iterative geographic information mining model reflecting the data evolution along the mining process. Associating the iterations, we propose a set of methodologies and integrate them into the processing onto solving the critical issues concerning collecting data, filtering irrelevant samples and extracting featured entities. According to the experiments, the contribution brings in a sound systematic solution to enrich the existing digital gazetteers as complete as Google Maps.

AB - In this paper, we propose a Geographic Information Mining framework to contribute some exploratory results concerning harvesting the featured place information entities from the Web. In the framework, we suggest an iterative geographic information mining model reflecting the data evolution along the mining process. Associating the iterations, we propose a set of methodologies and integrate them into the processing onto solving the critical issues concerning collecting data, filtering irrelevant samples and extracting featured entities. According to the experiments, the contribution brings in a sound systematic solution to enrich the existing digital gazetteers as complete as Google Maps.

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KW - Geographic information mining framework

KW - Place entity extraction

KW - Place-name dataset

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Extracting geographic features from the Internet: A geographic information mining framework

Abstract

Bibliographical note

Keywords

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