GIPSY: automated geographic indexing of text documents
Journal of the American Society for Information Science - Special issue: spatial information
Information Retrieval
Computing Geographical Scopes of Web Resources
VLDB '00 Proceedings of the 26th International Conference on Very Large Data Bases
Web-a-where: geotagging web content
Proceedings of the 27th annual international ACM SIGIR conference on Research and development in information retrieval
Detecting dominant locations from search queries
Proceedings of the 28th annual international ACM SIGIR conference on Research and development in information retrieval
Detecting geographic locations from web resources
Proceedings of the 2005 workshop on Geographic information retrieval
A Graph-Ranking Algorithm for Geo-Referencing Documents
ICDM '05 Proceedings of the Fifth IEEE International Conference on Data Mining
A confidence-based framework for disambiguating geographic terms
HLT-NAACL-GEOREF '03 Proceedings of the HLT-NAACL 2003 workshop on Analysis of geographic references - Volume 1
Extracting Geographic Context from the Web: GeoReferencing in MyMoSe
ECIR '09 Proceedings of the 31th European Conference on IR Research on Advances in Information Retrieval
Ranking web pages by associating keywords with locations
WAIM'13 Proceedings of the 14th international conference on Web-Age Information Management
Exploiting temporal information in Web search
Expert Systems with Applications: An International Journal
Exploiting location information for Web search
Computers in Human Behavior
Hi-index | 0.00 |
Most Web pages contain location information, which can be used to improve the effectiveness of search engines. In this paper, we concentrate on the focused locations, which refer to the most appropriate locations associated with Web pages. Current algorithms suffer from the ambiguities among locations, as many different locations share the same name (known as GEO/GEO ambiguity), and some locations have the same name with non-geographical entities such as person names (known as GEO/NON-GEO ambiguity). In this paper, we first propose a new algorithm named GeoRank, which employs a similar idea with PageRank to resolve the GEO/GEO ambiguity. We also introduce some heuristic rules to eliminate the GEO/NON-GEO ambiguity. After that, an algorithm with dynamic parameters to determine the focused locations is presented. We conduct experiments on two real datasets to evaluate the performance of our approach. The experimental results show that our algorithm outperforms the state-of-the-art methods in both disambiguation and focused locations determination.