Learning Ranking Functions for Geographic Information Retrieval Using Genetic Programming

Geographic Information Retrieval (GIR) has emerged as a new and promising tool for representation, storage, organisation of and access to geographic information. One of the current issues in GIR research is ranking of retrieved documents by both textual and geographic similarity measures. This paper describes an approach that learns GIR ranking functions using Genetic Programming (GP) methods based on textual statistics and geographic properties derived from documents and user queries. Our proposed approach has been applied to a large collection of geographic metadata docu ments. The experimental results show that the ranking functions learned using our method achieved significant improvement over existing ranking mechanisms in retrieval performance.