International Business Machines Corporation

Charles Kinston Williams

Frankie Elizabeth Patman Maguire

Leonard Arthur Shaefer, Jr

Frank Everett Whaley

Stephen John Watjen

Richard Theodore Gillam

Brian Eugene Macy

David Edward Biesenbach

Jeffrey James Jonas

Shudong Huang

Steven J Liddle

Bin Qin

Anna Khasin

Name matching using enhanced name keys is provided by receiving and parsing a queried name into name phrase(s), building a name key for the queried name, the name key for identifying matches between the queried name and candidate names in a database, and the name key including name phrase digraph bitmap signature(s) for the queried name, variant code(s) for the queried name, and pseudo-phonetic name phrase digraph bitmap signature(s) for the queried name, and performing a name matching comparison that includes comparing the queried name to each candidate name of the candidate names in the database, in which the built name key for the queried name is compared to a name key for the candidate name.

Name matching using enhanced name keys

A system creates a query name bitmap signature comprising at least one bigram for a query name. The system calculates a query name bitmap range for the query name based on a match threshold and a number of bigrams in the query name bitmap signature. The system creates a data list name bitmap signature comprising at least one bigram, for each of a plurality of data list names, and identifies a subset of the plurality of data list names. The subset comprises data list name bitmap signatures that are within the query name bitmap range. The system reduces a number of comparisons by comparing only the query name and each of the subset of the plurality of data list names when performing the analytical name search. The system also reduces the number of comparisons by reducing the number of bit positions that are compared.

Search performance using smart bitmap operations

According to one embodiment of the present invention, a method for name searching in mixed-entity lists is provided which comprises dividing a mixed list of entities into a plurality of entity-specific lists. A name to be searched is then categorized into a category and a specialized search logic is applied to the name to be searched. The specialized search logic is selected to be adapted to the category and uses a one of the entity-specific lists that corresponds to the category of the name to be searched. A shared search logic may also be employed, which is used for all names to be searched.

Entity-driven logic for improved name-searching in mixed-entity lists

A similarity between character strings is assessed by identifying first and second character strings as candidate similar character strings, determining a frequency of occurrence for at least one of the first and second character strings from a collection of character strings, and designating the first and second character strings as similar based on the determined frequency of occurrence.

String comparison results for character strings using frequency data

Provided are a computer implemented method, computer program product, and system for matching names. For a first bitmap distribution, it is determined whether a first bitmap signature of a query name and a second bitmap signature of a target name have a number of character n-grams overlapping that meet or exceed a threshold to generate a first preliminary value. For a second bitmap distribution that is different from the first bitmap distribution, it is determined whether a third bitmap signature of the query name and a fourth bitmap signature of the target name have a number of character n-grams overlapping that meet or exceed a threshold to generate a second preliminary value. The first preliminary value and the second preliminary value are combined, and, if the combination results in a value of true, it is determined that the query name and the target name are to be further processed.

Name search using multiple bitmap distributions

Provided are techniques for matching names. For a first bitmap distribution, it is determined whether a first bitmap signature of a query name and a second bitmap signature of a target name have a number of character n-grams overlapping that meet or exceed a threshold to generate a first preliminary value. For a second bitmap distribution that is different from the first bitmap distribution, it is determined whether a third bitmap signature of the query name and a fourth bitmap signature of the target name have a number of character n-grams overlapping that meet or exceed a threshold to generate a second preliminary value. The first preliminary value and the second preliminary value are combined, and, if the combination results in a value of true, it is determined that the query name and the target name are to be further processed.

A particular technique for classifying a name includes accessing a name; dividing the name into a series of first n-grams; forming multiple concatenated second n-grams by concatenating pairs of the first n-grams; for each of multiple groups, for each of the second n-grams, determining the term frequency-group frequency score; for each of the multiple groups, summing up the term frequency-group frequency scores for each second n-gram for that group; and determining a likelihood that the name belongs to one group of the multiple groups based on the summed scores, wherein a largest summed score indicates a greater likelihood that the name belongs to the one group.

Name classifier technique

A method and system for performing a search request for a name among a database including a plurality of names. In one implementation, the method includes receiving the search request on the name, determining a geographic location associated with the name, assigning a cultural classification to the name based on the geographic location associated with the name, and completing the search request by searching for the name among the plurality of names within the database based on the cultural classification assigned to the name.

Method and apparatus for assigning a cultural classification to a name using country-of-association information

A system and method for improved name matching using regularized name forms is presented. A regularization rule engine uses culture-specific regularization rules to iteratively convert candidate names and query names to a canonical form, which are regularized candidate names and regularized query names, respectively. The regularization rules are context-sensitive or context-free rules that pertain to a name's originating culture. Subsequently, a name search engine compares the regularized query name with the regularized candidate names and identifies the regularized candidate names that meet a particular regularization matching threshold. In turn, name search engine selects the candidate names that correspond to the identified regularized candidate names and provides the selected candidate names to a user.

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Oak Hill, VA, United States of America

Charles Kinston Williams