6449

Matching Points of Interest from Different Social Networking Sites

Tatjana Scheffler, Rafael Schirru, Paul Lehmann

KI 2012: Advances in Artificial Intelligence , Vol: 7526 , Pages: 245-248 , Springer, Berlin / Heidelberg , 2012
Valuable user-generated information about locations (points of interest, POIs) is stored in various online social media platforms. Merging the data associated with one POI is hard because the platforms lack common identifiers. In addition, user-generated data is commonly faulty or contradictory. Here we present an approach matching POIs from Qype and Facebook Places to their counterparts in OpenStreetMap. The algorithm uses different similarity measures taking the geographic distance of POIs into account as well as the string similarity of selected metadata fields, showing good results.

Show BibTex:

@incollection {
       abstract = {Valuable user-generated information about locations (points of interest, POIs) is stored in various online social media platforms. Merging the data associated with one POI is hard because the platforms lack common identifiers. In addition, user-generated data is commonly faulty or contradictory. Here we present an approach matching POIs from Qype and Facebook Places to their counterparts in OpenStreetMap. The algorithm uses different similarity measures taking the geographic distance of POIs into account as well as the string similarity of selected metadata fields, showing good results.},
       number = {}, 
       month = {9}, 
       year = {2012}, 
       title = {Matching Points of Interest from Different Social Networking Sites}, 
       journal = {}, 
       volume = {7526}, 
       pages = {245-248}, 
       publisher = {Springer, Berlin / Heidelberg}, 
       author = {Tatjana Scheffler, Rafael Schirru, Paul Lehmann}, 
       keywords = {Data Integration, Social Networks, User-Generated Content, Points of Interest},
       url = {http://dx.doi.org/10.1007/978-3-642-33347-7_24}
}