Metric details for natural-language spatial relations
ACM Transactions on Information Systems (TOIS)
Spatial Cognition and Computation
A Spatial Model Based on the Notions of Spatial Conceptual Map and of Object's Influence Areas
COSIT '99 Proceedings of the International Conference on Spatial Information Theory: Cognitive and Computational Foundations of Geographic Information Science
Assigning function tags to parsed text
NAACL 2000 Proceedings of the 1st North American chapter of the Association for Computational Linguistics conference
Geographic web search based on positioning expressions
Proceedings of the 2005 workshop on Geographic information retrieval
International Journal of Geographical Information Science
Prepositions in applications: A survey and introduction to the special issue
Computational Linguistics
Applying computational models of spatial prepositions to visually situated dialog
Computational Linguistics
How people describe their place: identifying predominant types of place descriptions
Proceedings of the 1st ACM SIGSPATIAL International Workshop on Crowdsourced and Volunteered Geographic Information
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The current keyword- and substring matches-based retrieval methods most search engines rely on to answer spatial queries ignore the more specific interpretations of spatial relations. Moreover, the use of the general preposition "at" in natural language queries results in underspecified locations. This paper examines the use of "at" in a set of crowdsourced place descriptions and develops a methodology for interpreting "at" as one of the more location-specific, closely related spatial prepositions "in", "on", or "by". The application of suggested schemas in the paper enables the interpretation of "at" according to the granularity level and classification type of the spatial feature it refers to. The crowdsourced results show that most people use "at" to locate themselves either in relation to buildings, or to (naturally or artificially) bounded outdoor areas (at street level). When used with building level reference features, "at" is more likely to be interpreted as "in" the feature (mostly indoors) and less so "by" it. For reference features at street level, "at" is more likely to be interpreted as "in" the reference feature's region (within a bounded outdoor area) and less so "on" it (e.g., a water surface). The results indicate it is possible to use the proposed methodology for enabling search engines to better rank the results returned to natural language spatial queries by appropriately interpreting "at". The paper's research outcomes are an example of the use of crowdsourced information for improving the interaction between users and services.