World explorer: visualizing aggregate data from unstructured text in geo-referenced collections
Proceedings of the 7th ACM/IEEE-CS joint conference on Digital libraries
Towards automatic extraction of event and place semantics from flickr tags
SIGIR '07 Proceedings of the 30th annual international ACM SIGIR conference on Research and development in information retrieval
Acquisition of a vernacular gazetteer from web sources
Proceedings of the first international workshop on Location and the web
Gazetiki: automatic creation of a geographical gazetteer
Proceedings of the 8th ACM/IEEE-CS joint conference on Digital libraries
DBpedia: a nucleus for a web of open data
ISWC'07/ASWC'07 Proceedings of the 6th international The semantic web and 2nd Asian conference on Asian semantic web conference
Landmark extraction: a web mining approach
COSIT'05 Proceedings of the 2005 international conference on Spatial Information Theory
The InfoAlbum image centric information collection
Proceedings of the International Conference on Web Intelligence, Mining and Semantics
Web Semantics: Science, Services and Agents on the World Wide Web
Using social media to find places of interest: a case study
Proceedings of the 1st ACM SIGSPATIAL International Workshop on Crowdsourced and Volunteered Geographic Information
Construction of a Japanese gazetteers for Japanese local toponym disambiguation
Proceedings of the 7th Workshop on Geographic Information Retrieval
Automatic gazetteer enrichment with user-geocoded data
Proceedings of the Second ACM SIGSPATIAL International Workshop on Crowdsourced and Volunteered Geographic Information
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Geographical gazetteers are necessary in a wide variety of applications. In the past, the construction of such gazetteers has been a tedious, manual process and only recently have the first attempts to automate the gazetteers creation been made. Here we describe our approach for mining accurate but large-scale multilingual geographic information by successively filtering information found in heterogeneous data sources (Flickr, Wikipedia, Panoramio, Web pages indexed by search engines). Statistically cross-checking information found in each site, we are able to identify new geographic objects, and to indicate, for each one, its name, its GPS coordinates, its encompassing regions (city, region, country), the language of the name, its popularity, and the type of the object (church, bridge, etc.). We evaluate our approach by comparing, wherever possible, our multilingual gazetteer to other known attempts at automatically building a geographic database and to Geonames, a manually built gazetteer.