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Abstract
Local search services allow a user to search for businesses that satisfy a given geographical constraint. In contrast to traditional web search engines, current local search services rely heavily on static, structured data. Although this yields very accurate systems, it also implies a limited coverage, and limited support for using landmarks and neighborhood names in queries. To overcome these limitations, we propose to augment the structured information available to a local search service, based on the vast amount of unstructured and semi‐structured data available on the web. This requires a computational framework to represent vague natural language information about the nearness of places, as well as the spatial extent of vague neighborhoods. In this paper, we propose such a framework based on fuzzy set theory, and show how natural language information can be translated into this framework. We provide experimental results that show the effectiveness of the proposed techniques, and demonstrate that local search based on natural language hints about the location of places with an unknown address, is feasible.
Notes
10. One can think of this straight‐line distance as the Euclidean distance. However, in practice usually the circle distance (i.e., the length of the shortest path between two points on the surface of a sphere) would be used instead, since locations are typically expressed as longitude and latitude coordinates.