Robust location search from text queries

Robust, global, address geocoding is challenging because there is no single address format that applies to all geographies, and in any case, users may not restrict themselves to well-formed addresses. Particularly in online mapping systems, users frequently enter queries with missing or conflicting information, misspellings, address transpositions, and other such variations. We present a novel system which handles these difficulties by using a combination of textual similarity and spatial coherence to guide a depth-first search over the large space of possible interpretations of a text query. The system robustly matches text subsequences of a query with text attributes (i.e., any text labels associated with the entity) in a spatial-entity database. Each matched attribute is associated with the pre-computed spatial union of all entities that have that attribute. Candidate results are formed by incremental spatial intersections of these unions. Experimental results demonstrate that our system is capable of supporting regions with widely differing address formats, without region-specific customization or training. Furthermore, we show that our system significantly outperforms commercial systems for unstructured location queries and queries containing errors.