Paper Info
Title
Deriving the Geographic Footprint of Cognitive Regions.
Abstract
The characterization of place and its representation in current Geographic Information System (GIS) has become a prominent research topic. This paper concentrates on places that are cognitive regions, and presents a computational framework to derive 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 consisting 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 of the historic center of Vienna and validate the results by comparing the derived region against a historical map of the city.
Year
DOI
Venue
2016
10.1007/978-3-319-33783-8_5
Lecture Notes in Geoinformation and Cartography
Keywords
Field
DocType
Geographic information retrieval,Cognitive regions,User generated content,Natural language processing,Machine learning,Semantic similarity
User-generated content,Semantic similarity,Geographic information system,Information retrieval,Computer science,Geographic information retrieval,Knowledge management,Footprint,Cognition,Semantic representation
Conference
ISSN
Citations 
PageRank 
1863-2246
0
0.34
References 
Authors
0
3
Name
Order
Citations
PageRank
Heidelinde Hobel1606.45
Paolo Fogliaroni2264.27
Andrew U. Frank3835332.71