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Silently resolving ambiguous locations in geocoding recommendation systems

IP.com Disclosure Number: IPCOM000237756D
Publication Date: 2014-Jul-09
Document File: 3 page(s) / 58K

Publishing Venue

The IP.com Prior Art Database

Abstract

Disclosed is a method to apply contextual information and heuristics to resolve as many ambiguities as is reasonable within a geocoding recommendation system without resorting to prompting the user. The method employs a new algorithm to detect and resolve ambiguities in location names.

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Silently resolving ambiguous locations in geocoding recommendation systems

A geocoding recommendation system generates a map based on a list of locations . Some of the input locations may be ambiguous. Different types of ambiguity include:

City/State ambiguity: New York the city vs. the state State/Country ambiguity: Georgia the US state vs. the country
Same name ambiguity: London, England vs. London, Ontario

Some mapping software prompts the user to resolve the ambiguity by asking, "Did you mean London, England, or London, Ontario?" Other mapping software allows the user to enter disambiguating information, such as "England" or "Ontario".

These known solutions do not work well in every environment because some software typically receives a long list of locations (e.g., locations of a company's sales offices). Repeatedly asking for user clarification in the user interface reduces the delight in the software and time to value.

The novel contribution is a method to apply contextual information and heuristics to resolve as many ambiguities as is reasonable

without resorting to prompting the user. The method employs a new algorithm to detect and resolve ambiguities in location names . The approach is to identify the ambiguities encountered and then allow the user to verify that the resolution is reasonable , that the system performed correctly and, if necessary, make corrections.

The algorithm uses a large table of locations that is structured as a tree to facilitate the rapid look -up of location names and the detection of ambiguities. When an ambiguity is detected, the algorithm searches for surrounding contextual information on which to base decisions.

For city/state ambiguity and state/country ambiguity, the method considers the following contextual factors:

1. Column title. For example, if the ambiguous location is "New York" and there is a column title that contains City (in any t...