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.
Bibliographical noteFunding 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 .
© 2019 Elsevier B.V.
- Geographic information mining
- Geographic information mining framework
- Place entity extraction
- Place-name dataset