International Business Machines Corporation

Frankie Elizabeth Patman Maguire

Leonard Arthur Shaefer, Jr

Charles Kinston Williams

Richard Theodore Gillam

Frank Everett Whaley

Jeffrey James Jonas

Brian Eugene Macy

Shudong Huang

Stephen John Watjen

David Edward Biesenbach

Friederike Mallchok

Anna Khasin

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 system for determining a similarity between a name phrase and a comparison name phrase, for each name in the name phrase, scores the name. The scoring is based on the field frequency of the name in a name database, where the field frequency indicates a given name frequency and/or a surname frequency in the database. The system uses the scoring to determine a transition from a given name to a surname in the name phrase. The system determines a primary given name and a primary surname in the name phrase based on the scoring and the transition. The system uses the primary given name and primary surname to determine a similarity between the name phrase and a comparison name phrase, where the comparison name phrase comprises a comparison given name and a comparison surname.

Scoring unfielded personal names without prior parsing

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 techniques for comparing names. A first phrase score is obtained by comparing a name phrase in a first name to a name phrase in a second name. A second phrase score is obtained by comparing another name phrase in the first name to another name phrase in the second name. An overall score is generated based on the obtained first phrase score and the obtained second phrase score. The overall score is updated based on comparing features of the first name with features of the second name.

Determining similarity of unfielded names using feature assignments

Provided are techniques for parsing a name. A name to be parsed is received. A culture of the name is identified. One or more name phrases from the name are identified. Statistics for the one or more name phrases are identified. It is determined whether to perform a first parsing technique that parses different types of name elements within at least one field of the name. In response to determining that the first parsing technique is to be performed, the name is parsed using the statistics and the first parsing technique. In response to determining that the first parsing technique is not to be performed, the name is parsed using the statistics and a second parsing technique.

Parsing culturally diverse names

Provided are techniques for identifying related names. A collection of names from different languages is stored, wherein each of the names has a native orthographic form and a romanized form. An input name is received in a known encoding scheme. An alphabet of the input name is determined based on the known encoding scheme. One or more romanized names are generated based on the query name and the determined query name alphabet. Culture-sensitive regularization rules are applied to create an additional romanized name. The one or more romanized names and the additional romanized name are matched against the romanized names in the collection of names from the different languages. Data store records that have romanized names that match the one or more romanized names or the additional romanized name are returned.

Identifying related names

A method includes receiving a search query that includes the input search string. The input search string includes a plurality of terms. The method includes performing a first comparison task to compare each of the plurality of terms to a list of category terms. The method also includes identifying a matched term as a category term when there is a match with respect to the first comparison task. The method includes identifying each of the plurality of terms that have not been identified as a category term to produce a set of naming terms. The method also includes filtering a database having a plurality of database entries based on each of the identified category terms to identify a reduced set of database entries. The method includes comparing at least one of the naming terms to the reduced set of database entries to identify at least one matching name.

Searching for a business name in a database

A method and system for differentiating types of data stored in a data collection. In one implementation, the method includes receiving a search request on a first type of data stored in the data collection; automatically differentiating data of the first type stored in the data collection from data of other types stored in the data collection; and completing the search request using data determined to be of the first type. Automatically differentiating data of the first type includes determining a type of each data entry in the data collection based only on tokens associated with the data entry.

Method and apparatus for automatically differentiating between types of names stored in a data collection

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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Washington, DC, United States of America

Frankie Elizabeth Patman Maguire