Geographical Information Retrieval with Ontologies of Place
COSIT 2001 Proceedings of the International Conference on Spatial Information Theory: Foundations of Geographic Information Science
GeoVSM: An Integrated Retrieval Model for Geographic Information
GIScience '02 Proceedings of the Second International Conference on Geographic Information Science
Indexing and ranking in Geo-IR systems
Proceedings of the 2005 workshop on Geographic information retrieval
Hybrid index structures for location-based web search
Proceedings of the 14th ACM international conference on Information and knowledge management
A query-aware document ranking method for geographic information retrieval
Proceedings of the 4th ACM workshop on Geographical information retrieval
A probabilistic topic-based ranking framework for location-sensitive domain information retrieval
Proceedings of the 32nd international ACM SIGIR conference on Research and development in information retrieval
Efficient retrieval of the top-k most relevant spatial web objects
Proceedings of the VLDB Endowment
Learning to rank for geographic information retrieval
Proceedings of the 6th Workshop on Geographic Information Retrieval
Reverse spatial and textual k nearest neighbor search
Proceedings of the 2011 ACM SIGMOD International Conference on Management of data
SIGSPATIAL Special
Extracting focused locations for web pages
WAIM'11 Proceedings of the 2011 international conference on Web-Age Information Management
SWORS: a system for the efficient retrieval of relevant spatial web objects
Proceedings of the VLDB Endowment
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Many Web queries contain both textual keywords and location words. When answering such queries, the association between the textual keywords and locations in a Web page should be taken into account. In this paper, we present a new ranking algorithm for location-related Web search, which is called MapRank. Its main idea is to extract the associations between keywords and locations in Web pages and further use them to improve ranking effectiveness. We first determine map each keyword with specific locations and form a set of pairs. Then, we compute the location-constrained score for each keyword and combine it into the ranking procedure. We conduct comparison experiments on a real dataset and use the metrics including MAP and NDCG to measure the performance of MapRank. The results show that MapRank is superior to previous methods with respect to different symbolic-location-related queries.