%0 Journal Article %1 ZHANG201957 %A Zhang, Ying %A Ma, Qunfei %A Chiang, Yao-Yi %A Knoblock, Craig %A Zhang, Xin %A Yang, Puhai %A Gao, Minghe %A Hu, Xiang %D 2019 %J Knowledge-Based Systems %K extraction information instituteclustering web %P 57 - 72 %R https://doi.org/10.1016/j.knosys.2019.02.031 %T Extracting geographic features from the Internet: A geographic information mining framework %U http://www.sciencedirect.com/science/article/pii/S0950705119300929 %V 174 %X 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.

@article{ZHANG201957, 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.}, added-at = {2021-01-19T12:09:12.000+0100}, author = {Zhang, Ying and Ma, Qunfei and Chiang, Yao-Yi and Knoblock, Craig and Zhang, Xin and Yang, Puhai and Gao, Minghe and Hu, Xiang}, biburl = {https://www.bibsonomy.org/bibtex/2c1a543a0a21e6840225a4d5024c01341/parismic}, description = {Extracting geographic features from the Internet: A geographic information mining framework - ScienceDirect}, doi = {https://doi.org/10.1016/j.knosys.2019.02.031}, interhash = {f781081597385c1744ac33666cdba1a4}, intrahash = {c1a543a0a21e6840225a4d5024c01341}, issn = {0950-7051}, journal = {Knowledge-Based Systems}, keywords = {extraction information instituteclustering web}, pages = {57 - 72}, timestamp = {2021-01-19T12:09:12.000+0100}, title = {Extracting geographic features from the Internet: A geographic information mining framework}, url = {http://www.sciencedirect.com/science/article/pii/S0950705119300929}, volume = 174, year = 2019 }