Statistical Models for Co-occurrence Data
Statistical Models for Co-occurrence Data
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
Semantic Grounding of Tag Relatedness in Social Bookmarking Systems
ISWC '08 Proceedings of the 7th International Conference on The Semantic Web
Proceedings of the 18th international conference on World wide web
Placing flickr photos on a map
Proceedings of the 32nd international ACM SIGIR conference on Research and development in information retrieval
An automatic translation of tags for multimedia contents using folksonomy networks
Proceedings of the 32nd international ACM SIGIR conference on Research and development in information retrieval
Towards ontology learning from folksonomies
IJCAI'09 Proceedings of the 21st international jont conference on Artifical intelligence
Information retrieval in folksonomies: search and ranking
ESWC'06 Proceedings of the 3rd European conference on The Semantic Web: research and applications
Emergent semantics from folksonomies: a quantitative study
Journal on Data Semantics VI
Uncovering locally characterizing regions within geotagged data
Proceedings of the 22nd international conference on World Wide Web
Hi-index | 0.00 |
Geographic information systems use databases to map keywords to places. These databases are currently most often created by using a top-down approach based on the geographic definitions. However, there is a problem with this approach in that these databases only contain location definitions such as addresses and place names, which does not allow for searches using keywords other than these words. Additionally, they do not give any information on the popularity, e.g., which is more popular among the places indexed by the same keyword. A bottom-up approach, based on the actual usage of words, can address these problems. We propose a method to aggregate tagging data and extract places related to a tag using the pair of a tag and a geo-tagged photo. We target the co-occurrence of a tag and the geolocation and represent the places related to a tag as a probability distribution over the longitudes and latitudes. We applied our method to data on the photo sharing service Flickr and experimentally confirmed that our method made it possible to highly-accurately extract places related to tags. Our direct bottom-up approach enables the extraction of place information that is not obtained by using traditional top-down approaches.