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Talks and Poster Presentations (with Proceedings-Entry):

H. Hobel, P. Fogliaroni, A. Frank:
"Deriving the Geographic Footprint of Cognitive Regions";
Talk: 19th AGILE International Conference on Geographic Information Science, Helsinki, Finland; 2016-06-14 - 2016-06-17; in: "Proceedings", T. Sarjakoski, M. Santos, T. Sarjakoski (ed.); Springer, (2016), ISBN: 978-3-319-33782-1; 67 - 84.



English abstract:
The characterization of place and its representation in current Geographic Information System (GIS) has become a promoinent research topic. The paper concentrates on places that are cognitive regions, and presents a computational framework to deriuve the geographic footprint of these regions. The main idea is to use Natural Language Processing (NLP) tools to identify unique geographic features from User Generated Content (UGC) sources consiting of textual descriptions of places. These features are used to detect on a map an initial area that the descriptions refer to. A semantic representation of this area is extracted from a GIS and passed over to a Machine Learning (ML) algorithm that locates other areas according to semantic similarity. As a case study, we employ the proposed framework to derive the geographic footprint on the historic center of Vienna and validate the results by comparing the derived region against a map of the city.

German abstract:
The characterization of place and its representation in current Geographic Information System (GIS) has become a promoinent research topic. The paper concentrates on places that are cognitive regions, and presents a computational framework to deriuve the geographic footprint of these regions. The main idea is to use Natural Language Processing (NLP) tools to identify unique geographic features from User Generated Content (UGC) sources consiting of textual descriptions of places. These features are used to detect on a map an initial area that the descriptions refer to. A semantic representation of this area is extracted from a GIS and passed over to a Machine Learning (ML) algorithm that locates other areas according to semantic similarity. As a case study, we employ the proposed framework to derive the geographic footprint on the historic center of Vienna and validate the results by comparing the derived region against a map of the city.

Keywords:
Machine Learning, Natural Language Processing, Geographic Footprint, Cognitive Regions, USer Generated Content

Created from the Publication Database of the Vienna University of Technology.