Abstract
Geographic Information Retrieval (GIR) is a specialized branch of traditional Information Retrieval (IR), which deals with the information related to geographic locations. One of the main challenges of GIR is to quantify the spatial relevance of documents and generate a pertinent ranking of the results according to the spatial information needs of user. Most of the current methods judge the relevance of documents just based on textual and spatial similarity with the query, and ranked the results with a linear combination of these similarity measures. We consider relevance ranking as a much more dynamic problem stemming from real world application such as location based mobile services, where user not only seek information but there is a decision making involved with the search i.e. to visit the location. In this paper we discuss current ranking phenomenon in geographic information retrieval, present different relevant parameters based on our initial study, and argue for the need of a formal relevance framework and ranking mechanism for geographical information retrieval. We approach GIR ranking as a spatial decision problem to support user’s activity, and propose the idea to explore decision-theoretic framework and probabilistic representation for geo relevance formalization.
Highlights
Information Retrieval is a discipline which deals with the storage, organization, representation and access of information
This problem especially applies on geographic Information retrieval which is a specialized branch of Information Retrieval (IR) that deals with the retrieval of documents with geographic significance
All the georeferenced documents represent real world physical objects, and approaching information based on their georeferences can be important in several contexts, and the relevance judgment requires interpretation of implicit information enclosed in documents and queries to provide suitable response to queries
Summary
Information Retrieval is a discipline which deals with the storage, organization, representation and access of information. In traditional IR ranking models, the effectiveness of results depends on the appropriateness of a user query, and the performance is limited when desired results have inherent information requirements This problem especially applies on geographic Information retrieval which is a specialized branch of IR that deals with the retrieval of documents with geographic significance. While typical information retrieval measures have been explored to compute thematic importance, geographical importance has been reduced to computing the similarity between two geographic locations, one associated with the query and the other with the document These hybrid ranking methods do not consider the document specific spatial properties and its connectivity to other documents and are less adaptive to spatial and contextual information need especially for a user in mobile environment.
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