# Patent

## Title
Parsing information in data records and in different languages

## Abstract

Embodiments of systems and methods for comparing attributes of a data record are presented herein. In some embodiments, a weight is based on a comparison of the name (or other) attributes of data records. In some embodiments, an information score may be calculated for each of two name attributes to be compared to get an average information score for the two name attributes. The two name attributes may then be compared against one another to generate a weight between the two attributes. This weight can then be normalized to generate a final weight between the two business name attributes. Comparing attributes according to embodiments disclosed herein can facilitate linking data records even if they comprise attributes in languages which do not use the Latin alphabet.

## Background
CROSS-REFERENCE TO RELATED APPLICATIONS
This application claims priority from U.S. Provisional Application No. 60/920,612, filed Mar. 29, 2007, entitled “METHOD AND SYSTEM FOR PARSING LANGUAGES,” which is fully incorporated by reference herein.


TECHNICAL FIELD
This invention relates generally to the parsing of information. Even more particularly, embodiments of this invention relate to systems and methods for the parsing of information in certain languages, including Asian languages.
BACKGROUND
In today's day and age, the vast majority of businesses retain extensive amounts of data regarding various aspects of their operations, such as inventories, customers, products, etc. Data about entities, such as people, products, parts or anything else may be stored in digital format in a data store such as a computer database. These computer databases permit the data about an entity to be accessed rapidly and permit the data to be cross-referenced to other relevant pieces of data about the same entity. The databases also permit a person to query the database to find data records pertaining to a particular entity, such that data records from various data stores pertaining to the same entity may be associated with one another.
A data store, however, has several limitations which may limit the ability to find the correct data about an entity within the data store. The actual data within the data store is only as accurate as the person who entered the data, or an original data source. Thus, a mistake in the entry of the data into the data store may cause a search for data about an entity in the database to miss relevant data about the entity because, for example, a last name of a person was misspelled or a social security number was entered incorrectly, etc. A whole host of these types of problems may be imagined: two separate record for an entity that already has a record within the database may be created such that several data records may contain information about the same entity, but, for example, the names or identification numbers contained in the two data records may be different so that it may be difficult to associate the data records referring to the same entity with one other.
For a business that operates one or more data stores containing a large number of data records, the ability to locate relevant information about a particular entity within and among the respective databases is very important, but not easily obtained. Once again, any mistake in the entry of data (including without limitation the creation of more than one data record for the same entity) at any information source may cause relevant data to be missed when the data for a particular entity is searched for in the database. In addition, in cases involving multiple information sources, each of the information sources may have slightly different data syntax or formats which may further complicate the process of finding data among the databases. An example of the need to properly identify an entity referred to in a data record and to locate all data records relating to an entity in the health care field is one in which a number of different hospitals associated with a particular health care organization may have one or more information sources containing information about their patient, and a health care organization collects the information from each of the hospitals into a master database. It is necessary to link data records from all of the information sources pertaining to the same patient to enable searching for information for a particular patient in all of the hospital records.
There are several problems which limit the ability to find all of the relevant data about an entity in such a database. Multiple data records may exist for a particular entity as a result of separate data records received from one or more information sources, which leads to a problem that can be called data fragmentation. In the case of data fragmentation, a query of the master database may not retrieve all of the relevant information about a particular entity. In addition, as described above, the query may miss some relevant information about an entity due to a typographical error made during data entry, which leads to the problem of data inaccessibility. In addition, a large database may contain data records which appear to be identical, such as a plurality of records for people with the last name of Smith and the first name of Jim. A query of the database will retrieve all of these data records and a person who made the query to the database may often choose, at random, one of the data records retrieved which may be the wrong data record. The person may not often typically attempt to determine which of the records is appropriate. This can lead to the data records for the wrong entity being retrieved even when the correct data records are available. These problems limit the ability to locate the information for a particular entity within the database.
To reduce the amount of data that must be reviewed, and prevent the user from picking the wrong data record, it is also desirable to identify and associate data records from the various information sources that may contain information about the same entity. There are conventional systems that locate duplicate data records within a database and delete those duplicate data records, but these systems may only locate data records which are substantially identical to each other. Thus, these conventional systems cannot determine if two data records, with, for example, slightly different last names, nevertheless contain information about the same entity. In addition, these conventional systems do not attempt to index data records from a plurality of different information sources, locate data records within the one or more information sources containing information about the same entity, and link those data records together. Consequently, it would be desirable to be able to associate data records from a plurality of information sources which pertain to the same entity, despite discrepancies between attributes of these data records and be able to assemble and present information from these various data records in a cohesive manner. In practice, however, it can be extremely difficult to provide an accurate, consolidated view of information from a plurality of information sources. The challenge can be even greater in some cases where data records from the plurality of information sources contain more than one language.
SUMMARY OF THE DISCLOSURE
Today's data records may be in a variety of languages other than English. In some cases, attributes of data records may be in a language which does not use the Latin alphabet at all. These languages do not necessarily obey the rules, paradigms or convention of languages written in the Latin alphabet, making attributes in these languages difficult to parse. These issues are particularly germane when it comes to Asian languages such as Japanese, Korean, Chinese, etc. which utilize many different code sets or alphabets in addition to Latin characters, ideographic characters, etc. Further complicating the parsing of these types of languages is that whitespace may not be used to separate portions of names, making the parsing of such names into tokens extremely difficult without the application of an extensive knowledge base for the language.
Embodiments disclosed herein can provide systems and methods that can efficiently parse information contained in data records from a plurality of information sources that may employ two or more languages, including languages of Asian origin. Particularly, embodiments of systems and methods for comparing attributes of a data record are presented herein. In some embodiments, a weight is generated based on a comparison of the name (or other) attributes of data records. In some embodiments, an information score may be calculated for each of two name attributes to be compared to get an average information score for the two name attributes. The two name attributes may then be compared against one another to generate a weight between the two attributes. This weight can then be normalized to generate a final weight between the two business name attributes.
In one embodiment, a method for comparing attributes comprises calculating an average information score for a first attribute and a second attribute, generating a weight for the first attribute and the second attribute, and normalizing the weight based on the average information score. The weight may be generated by comparing each of a set of tokens of the first attribute to each of a set of tokens of the second attribute and comparing each pair of tokens. In one embodiment, this can be done by determining a current match weight for the pair of tokens, determining a first previous match weight corresponding to the pair of tokens, determining a second previous match weight corresponding to the set of tokens, setting the weight to the current match weight if the current match weight is greater than the first previous match weight or the second previous match weight, and setting the weight to the greater of the first previous match weight or the second previous match weight if either the first previous match weight or the second previous match weight is greater than the current match weight.
Accordingly, embodiments disclosed herein can link data records even if they comprise attributes in languages which do not use the Latin alphabet. Other features, advantages, and objects of the disclosure will be better appreciated and understood when considered in conjunction with the following description and the accompanying drawings.



BRIEF DESCRIPTION OF THE DRAWINGS
The drawings accompanying and forming part of this specification are included to depict certain aspects of the invention. A clearer impression of the invention, and of the components and operation of systems provided with the invention, will become more readily apparent by referring to the exemplary, and therefore non-limiting, embodiments illustrated in the drawings. Wherever possible, the same reference numbers will be used throughout the drawings to refer to the same or like features (elements). The drawings are not necessarily drawn to scale.
FIG. 1 depicts one embodiment of an example infrastructure.
FIGS. 2A and 2B depict a representation of two embodiments of data records.
FIG. 3 depicts a flow diagram for one embodiment of comparing data records.
FIG. 4 depicts a flow diagram for one embodiment of a method for comparing attributes of a data record.
FIGS. 5A and 5B depicts a flow diagram for one embodiment of a method for determining a weight for two attributes.
FIG. 6 depicts a flow diagram for one embodiment of a method for determining values used in the computation of a weight.
FIG. 7 depicts an example of a table for use in describing an example of the application of one embodiment of the present invention.
FIGS. 8A and 8B depict examples of tables for use in describing an example of the application of one embodiment of the present invention.
FIG. 9 depicts a flow diagram for one embodiment of parsing an Asian language.
FIG. 10 depicts an example of some traditional characters and their simplified equivalents.
FIG. 11 depicts an example of grouping modifiers or other ungrouped characters.
FIG. 12 depicts a histogram plotting the statistics of the number of tokens that one embodiment of the parsing disclosed herein can create for each name from a sample set.
FIG. 13 depicts a histogram plotting the distribution of the character lengths of the parsed tokens of FIG. 12.



DETAILED DESCRIPTION
Preferred embodiments and the various features and advantageous details thereof are explained more fully with reference to the examples illustrated in the accompanying drawings. Descriptions of well known computer hardware and software, including programming and data processing techniques, are omitted so as not to unnecessarily obscure the invention in detail. Skilled artisans should understand, however, that the detailed description and the specific examples, while disclosing preferred embodiments of the invention, are given by way of illustration only and not by way of limitation. Various substitutions, modifications, additions or rearrangements within the scope of the underlying inventive concept(s) will become apparent to those skilled in the art after reading this disclosure.
Some embodiments disclosed herein can leverage an embodiment of a system and method for indexing information about entities from different information source, as described in U.S. Pat. No. 5,991,758, issued Nov. 23, 1999, which is incorporated herein by reference. Some embodiments disclosed herein can leverage an embodiment of a master data management system and method for indexing information about entities with respect to hierarchies, as disclosed in U.S. patent application Ser. No. 11/656,111, filed Jan. 22, 2007, which is also incorporated herein by reference.
FIG. 1 is a block diagram illustrating an example infrastructure of one embodiment of entity processing system 30. Entity processing system 30 may include Identity Hub 32 that processes, updates, or stores data pertaining to data records about one or more entities from one or more information sources 34, 36, 38 and responds to commands or queries from a plurality of operators 40, 42, 44, where the operators may be human users and/or information systems. Identity Hub 32 may operate with data records from a single information source or, as shown, data records from multiple information sources The entities tracked using embodiments of Identity Hub 32 may include, for example, patients in a hospital, participants in a health care system, parts in a warehouse, or any other entities that may have data records and information contained in data records associated therewith. Identity Hub 32 may be one or more computer systems with at least one central processing unit (CPU) 45 executing computer readable instructions (e.g., a software application) stored on one or more computer readable storage media to perform the functions of Identity Hub 32, Identity Hub 32 may also be implemented using hardware circuitry or a combination of software and hardware as would be understood by those skilled in the art.
In the example of FIG. 1, Identity Hub 32 may receive data records from information sources 34, 36, 38 as well as write corrected data back into information sources 34, 36, 38. The corrected data communicated to information sources 34, 36, 38 may include information that was correct, but has changed, information about fixing information in a data record, or information about links between data records.
In addition, one of operators 40, 42, 44 may transmit a query to Identity Hub 32 and receive a response to the query back from Identity Hub 32. Information sources 34, 36, 38 may be, for example, different databases that may have data records about the same entities. For example, in the health care field, each information source 34, 36, 38 may be associated with a particular hospital in a health care organization and the health care organization may use Identity Hub 32 to relate the data records associated with the plurality of hospitals so that a data record for a patient in Los Angeles may be located when that same patient is on vacation and enters a hospital in New York. Identity Hub 32 may be located at a central location and information sources 34, 36, 38 and users 40, 42, 44 may be located remotely from Identity Hub 32 and may be connected to Identity Hub 32 by, for example, a communications link, such as the Internet or any other type communications network, such as a wide area network, intranet, wireless network, leased network, etc.
In some embodiments, Identity Hub 32 may have its own database that stores complete data records in Identity Hub 32. In some embodiments, Identity Hub 32 may also only contain sufficient data to identify a data record (e.g., an address in a particular data source 34, 36, 38) or any portion of the data fields that comprise a complete data record so that Identity Hub 32 can retrieve the entire data record from information source 34, 36, 38 when needed. Identity Hub 32 may link data records together containing information about the same entity utilizing an entity identifier or an associative database separate from actual data records. Thus, Identity Hub 32 may maintain links between data records in one or more information sources 34, 36, 38, but does not necessarily maintain a single uniform data record for an entity.
In some embodiments, Identity Hub 32 may link data records in information sources 34, 36, 38 by comparing a data record (received from an operator, or from a data source 34, 36, 38) with other data records in information sources 34, 36, 38 to identify data records which should be linked together. This identification process may entail a comparison of one or more of the attributes of the data records with like attributes of the other data records. For example, a name attribute associated with one record may be compared with the name of other data records, social security number may be compared with the social security number of another record, etc. In this manner, data records which should be linked may be identified.
It will be apparent to those of ordinary skill in the art, that information sources 34, 36, 38 and operators 40, 42, 44 may be affiliated with similar or different organizations or owners. For example, data source 34 may be affiliated with a hospital in Los Angeles run by one health care network, while data source 36 may be affiliated with a hospital in New York run by another health care network. Thus, the data records of each of data sources may be of a different format, different languages, etc.
This may be illustrated more clearly with reference to FIGS. 2A and 2B, depicting two embodiments of example data records. Each of these data records 200, 202 has a set of fields 210 corresponding to a set of attributes of each of the data records. For example, one of the attributes of each of the records 200 may be a name, another attribute may be a taxpayer number, etc. It will be apparent that an attribute may comprise multiple fields 210 of data records 200, 202. For example, an address attribute of data record 202 may comprise fields 210c, 210d and 210e, the street, city and state fields, respectively.
However, each of data records 200, 202 may have a different format. For example, data record 202 may have a field 210 for the attribute of “Insurer”, while data record 200 may have no such field. Moreover, similar attributes may have different formats as well. For example, name field 210b in record 202 may accept the entry of a full name, while name field 210a in record 200 may be designed to allow entry of a name of a limited length. Such discrepancies may be problematic when comparing two or more data records (e.g., attributes of data records) to identify data records which should be linked. For example, the name “Bobs Flower Shop” is similar, but not exactly the same as “Bobs Very Pretty Flower Shoppe.” Furthermore, a typo or mistake in entering data for a data record may also affect the comparison of data records and thus the results thereof (e.g., comparing the name “Bobs Pretty Flower Shop” with “Bobs Pretty Glower Shop” where “Glower” resulted from a typo in entering the word “Flower”).
To deal with these possibilities, a variety of name comparison techniques may be utilized to generate a weight based on the comparison (e.g., similarity) of names in different records where this weight could then be utilized in determining whether two records should be linked, including various phonetic comparison methods, weighting based on frequency of name tokens, initial matches, nickname matches, etc. In some embodiments, the tokens of the name attribute of each record would be compared against one another, using methodologies to match the tokens (e.g., if the tokens matched exactly, phonetically, etc.). These matches could then be given a weight, based upon the determined match (e.g., an exact match is given a first weight, while a certain type of initial match is given a second weight, etc.). These weights could then be aggregated to determine an overall weight for the degree of match between the name attribute of two data records.
These techniques were not without their various problems, however, especially when applied to business names. This is because business names may present a number of fairly specific problems as a result of their nature. Some business names are very short (e.g., “Quick-E-Mart”) while others are very long (e.g., “San Francisco's Best Coffee Shop”). Additionally, business names may frequently use similar words (e.g., “Shop”, “Inc.”, “Co.”) which, though identical, should not weigh heavily in any heuristic for comparing these names. Furthermore, acronyms are frequently used in business names, for example a business named “New York City Bagel” may frequently be entered into a data record as “NYC Bagel.”
In many cases, the algorithms employed to compare business names would not take into account these specific peculiarities when comparing business names. Prior to the embodiments disclosed herein, there was no support for acronyms, the frequency of certain words in business names was not accounted for and the ordering of tokens within a business name was not accounted for (e.g., the name “Clinic of Austin” may have been deemed virtually identical to “Austin Clinic”). In fact, in some cases, two name attributes which only partially matched may have received a higher weight than two names attributes which match exactly. Consequently, match weights between business names were skewed, and therefore skewed the comparisons between data records themselves. Thus, it would be desirable to utilize comparison algorithms which can generate weights that better reflect the similarity between name attributes of different records.
In embodiments disclosed herein, comparison algorithms may generate a weight based on a comparison of the name attributes of data records. More specifically, an information score may be calculated for each of two name attributes to be compared to get an average information score for the two name attributes. The two name attributes may then be compared against one another to generate a weight between the two attributes. This weight can then be normalized to generate a final weight between the two business name attributes.
To aid in an understanding of the systems and methods of the embodiments disclosed herein, it will be helpful to present an example of a methodology for identifying records pertaining to the same entity which may utilize these systems and methods. FIG. 3 depicts one such embodiment. A set of data records for comparison may be given for evaluation at step 310. These data records may include, for example, one or more new data records to compare to a set of existing data records (which may already exist in, for example, information sources 34, 36, 38 or which may be provided as well). At step 320, the data records for comparison may be standardized. This standardization may comprise the standardization of attributes of a data record into a standard format, such that subsequent comparisons between like attributes of different data records may be performed according to this standard format. For example, field 210a of the name attribute of data record 200 may be evaluated to produce a set of tokens for the name attribute (e.g., “Bobs”, “Pretty”, “Flower” and “Shop”) and these tokens can be concatenated in accordance with a certain form to produce a standardized attribute (e.g., “Bobs:Pretty:Flower:Shop”) such that the standardized attribute may subsequently be parsed to generate the tokens which comprise the name attribute. It will be apparent to those skilled in the art that each of the attributes of the data records to be compared may be tokenized according to a different format, a different set of semantics or lexicon, etc.
In one embodiment, when names are standardized, consecutive single tokens are combined into tokens (e.g., I.B.M. becomes IBM) and substitutions are performed (e.g., “Co.” is replaced by “Company”, “Inc.” is replaced by “Incorporated”, etc.). An equivalence table comprising abbreviations and their equivalent substitutions may be stored in a database associated with Identity Hub 32. Pseudo code for one embodiment of standardizing business names in accordance with embodiments of the present invention is as follows:












BusinessNameParse(inputString, equivalenceTable):


 STRING outputString


 for c in inputString:


   if c is a LETTER or a DIGIT:


     copy c to outputString


   else if c is one of the following characters [&,’,‘] (ampersand, single 


 quote, back quote)


     skip c (do not replace with a space)


   else //non-ALPHA-DIGIT [&,’,‘] character


     if the last character in output string is not a space, copy a 


 space to output string.


 //Now extract the tokens.


 tokenList = [ ]


 For token in outputString //outputString is a list of tokens separated by 


 spaces


   If (token is a single character and it is followed by one or more 


 single characters)


     Combine the singletokens into a single token


   If (equivalenceTable maps token)


     Replace token with its equivalence.


   Append token to tokenList.


 Return tokenList









In some cases, attributes of data records may be in a variety of languages other than English. In some cases, attributes of data records may be in a language which does not use the Latin alphabet altogether. As these languages do not necessarily obey the rules, paradigms or convention of languages written in the Latin alphabet, attributes in these languages may be difficult to parse into tokens. These issues are particularly germane when it comes to Asian languages such as Japanese, Korean, Chinese, etc. which utilize many different code sets or alphabets in addition to Latin characters, ideographic characters, etc. Further complicating the parsing of these types of languages is that whitespace may not be used to separate portions of names, making the parsing of such names into tokens extremely difficult without the application of an extensive knowledge base for the language.
Embodiments disclosed herein can link data records even if they comprise attributes in these languages. Particularly, embodiments disclosed herein can parse attributes, such as names, in various languages, including Asian languages, into tokens. Some embodiments may provide methods and systems for separating attributes in Asian languages into tokens. Some embodiments may provide methods and systems for parsing Japanese names (e.g., a name comprising at least one character in a Japanese alphabet or character set, but which may also comprise characters in any other alphabet) into one or more tokens. Some embodiments may provide methods and systems for introducing whitespace into a Japanese name attribute such that the Japanese name can be parsed into one or more tokens based on the whitespace.
It will be understood that, although some embodiments are described herein in the context of parsing a name attribute of a data record into one or more tokens so that these attributes may be compared to generate a match score between data records, embodiments of the present disclosure may be applied to any situation where it is desired to parse a name or any other attribute or character string in just about any languages.
Turning now to FIG. 9, a flow diagram for one embodiment of a method for the parsing of a character sequence such as a name in Japanese (e.g., having at least one Japanese character) is depicted. At step 910, a Japanese name is received. Any half-width Katakana characters, which represent Latin characters, in the received name are translated into their Latin equivalents at step 920. In one embodiment, each of the name characters of the name may be represented by a code, for example according to the a code promulgated by the Unicode consortium (e.g., Unicode Transformation Format (UTF) or Universal Character Set (UCS)) or the like, for example, each character may be represented by a code point in UCS-4, UTF-32, UTF-16, UCS-2, UTF-EBDIC, UTF-8, UTF-7, etc. Thus, each of the code points for each of the characters of the name may be compared to a range of code points corresponding to half-width Katakana characters which are equivalent to Latin characters to see if the character is a half-width Katakana character. If the character is a half-width Katakana character which is equivalent to a Latin character, it may be replaced (e.g., the code point corresponding to the character replaced with another code point) with an equivalent Latin character. Latin equivalents in the half-width Katakana code range should be translated to the Latin range. Specifically, in one embodiment, an character whose code portion falls within the range 65281 to 65370 is translated to the respective code point in the range of code points from 33 to 122 (e.g., if a code point of 65281 is found it is replaced by the code point 33, if a code point of 65300 is found it is replaced by the code point 52, etc.).
To further facilitate comparison of name attributes then, all the lower case Latin characters in the name may then be translated to their uppercase equivalents (e.g., “a” gets translated to “A”) at step 930. Again, in one embodiment, this may comprise a replacement of any code points representing lower case Latin characters in the name with the respective code point representing an uppercase version of the same Latin character.
At step 940, any delimiters within the name may be translated to whitespace. The definition of which characters are delimiters may either be a standard list of delimiters corresponding to the set of code points utilized or may be a proprietary list of delimiters (e.g., which may comprise a standard list of delimiters plus specific delimiters for the language of the name, such as Japanese specific delimiters or the like or any other desired delimiters). For example, a list of code points corresponding to delimiters may be stored in a database such that at step 930 each of the code points of the characters of the name may be compared to the list of delimiters and if the code point of a character of the name correspond to a code point in the list of delimiters the code point of the character of the name may be replaced with the code point corresponding to whitespace.
Then, at step 950 any traditional characters in the name may be translated to a simplified equivalent. Again, in one embodiment, a traditional mapping of code points corresponding to traditional characters to code points corresponding to their simplified equivalent may be used to translate a traditional character to its simplified equivalent by replacing the code point corresponding to the traditional character with the code point corresponding to the equivalent simplified character. In another embodiment, however, the mapping between traditional characters and simplified characters may be a proprietary mapping and a database may store a list of code points of traditional characters where each of these code points is associated with a code point for an equivalent simplified character. Using this list then, traditional characters in the name may be identified by comparing code points of the name to the list and the identified traditional characters translated to simplified characters by replacing their code point with the associated code for the equivalent simplified character.
After traditional characters are translated to their simplified equivalent at step 950, whitespace may be inserted between characters of different types at step 960. For example, a Japanese name may encompass characters of many different types: Latin, Hiragana, Katakana, phonetic Katakana, half-width Katakana, among many others. Thus, if two characters which are of different types (e.g., different character sets) whitespace may be inserted between the two characters in the name. In one embodiment, ranges of code points may designate character sets (e.g., Latin character may comprise the code point range from 1 to 255, Hiragana may comprise the character the code point range of 12352 to 12447, etc.). Thus, using specified or otherwise defined code point ranges, adjacent characters within different code point ranges may be identified and whitespace inserted in the name between these two characters.
Following the insertion of whitespace between characters of different types in step 960, ideographic characters may be identified in the name and these ideographic characters set off by whitespace (e.g., whitespace inserted before or after the character) at step 970. In one embodiment, a list may be kept comprising a set of code point which correspond to ideographic characters. If a character is identified in the name which is an ideographic character (e.g., by comparing the code point corresponding to the character to the code points comprising the list of ideographic characters), whitespace may be inserted into the name before the ideographic character and after the ideographic character (e.g., if the ideographic character is not the last character of the name).
The name may then be parsed into a set of token at step 990. In one embodiment, this parsing may be done based on whitespace in the name. Thus, whitespace may designate the end of one token of the name or the beginning of another token of the name. In this manner then, a set of tokens may be obtained from a Japanese name. Additionally, by applying other embodiments of the invention (which may, for example, utilize different code points or code point ranges) names in other Asian languages such as Korean or Chinese may likewise be separated into a set of tokens. One embodiment for comparing Japanese names or attributes comprises parsing the names into tokens utilizing a table-driven translation technique. In one embodiment, the table-driven translation technique can be performed via Unicode mapping or umap-type capability for mapping/filtering between different encoding and character sets. Other translation techniques may also be used.
Thus, in one embodiment, a Japanese name can be parsed into tokens as follows:

    
    
        Translate half-width Katakana to Latin.
        Translate Latin equivalents in the half-width Katakana code range in decimal (65281, 65370) to the Latin range in decimal (33, 122).
        Translate Latin lower case to upper case.
        Translate delimiters to white space.
        Add Japanese-specific delimiters to the standard list where applicable. While not an exhaustive list examples drawn from sample data include:
    
    
















Decimal code
Character



















34
“



38
&



39
‘



40
(



41
)



42
*



43
+



44
,



45
-



46
.



47
/



58
:



59
;



95
—



65381
•



12539
●



12288










In one embodiment, there is a table of 222 of these translations. An example is shown in FIG. 10 where the traditional character is in the second column and the simplified equivalent is in the first column.
A second technique may be applied when code sets change. An example of that is where the first three characters are Katakana and the last four are Kanji. Here, two tokens would be formed. This will also help when there is imbedded Latin such as NTT or S S . The difference between the first and the second is that the first uses actual Latin code points where the second uses their half-width equivalents.
The following code point ranges are exemplary and do not exhaust the total range or ranges available:















Character-set name
Decimal range




















Latin
1
255



Hiragana
12352
12447



Katakana
12448
12543



Katakana phonetic
12784
12799



Half - kat
65280
65519



CJK - unified
19968
40895



CJK - a ext
13312
19903



CJK - b ext
131072
173791



CJK compatible
63744
64255



CJK - b comp
194560
195103



CJK - radicals
11904
12031



CJK - strokes
12736
12783










Here, the first column is the character-set name and the second and third are the decimal ranges. CJK—unified is what is called Kanji when applied to Japanese data.
The following distribution are from statistics regarding characters in a sample set of Japanese business name data:














Character-set name
Occurrence



















Latin
51288



Hiragana
5159



Katakana
154766



Katakana phonetic
0



Half - kat
20543



CJK - unified
226443



CJK - a ext
0



CJK - b ext
0



CJK compatible
5



CJK - b comp
0



CJK - radicals
0



CJK - strokes
0










In this case, most are Kanji (CJK—unified) and Katakana.
In this example, certain characters in the data did not fall into any of these groups. They were mostly modifiers and these characters were grouped characters with the characters which precede it (the most common of these was the iterator character  (i.e.,  means ). Almost any method of grouping modifiers or other ungrouped characters may be applied.
The aforementioned two techniques may be combined. For example, they can be applied to a set of sample names so that ()  parses to {}, and ·—becomes {, —}, etc. A sample of the parsing is shown in FIG. 11. FIG. 12 is a histogram depicting the statistics of the number of tokens the parsing created for each name from the sample set of data. As it can be seen from FIG. 12, approximately 16,608 of the names were left as single tokens from 44,681 sample names. Thus, about 63% of the names were parsed into at least two tokens. The majority is two-token names and the average is 1.94 parsed tokens per name. FIG. 13 is a histogram depicting the distribution of the character lengths of the parsed tokens, with an average of 5.1 characters per token. This compares to an average length of 10.3 characters in the original data. To sum up, in this embodiment, the steps for the name parsing comprise:

    1. translate the half-width characters to their Latin equivalents—those that have them.
    2. translate lower-case Latin to upper-case Latin.
    3. translate special characters to whitespace (delimiter).
    4. insert whitespace whenever characters change from one code set to another—the ranges should also be table-driven—unless the change is to a modifier.
    5. insert whitespace before and after (if needed) any ideographic characters; and
    6. parse to tokens on white space.


It will be noted that any limiting language designating portions of or items within embodiments described above will be understood to only apply in view of, or with respect to, the particular embodiments and will not be understood to apply generally to the systems and methods of the present invention.
Referring back to FIG. 3, no matter the techniques used, once the attributes of the data records to be compared have been standardized at step 320, a set of candidates may be selected to compare to the new data record at step 330. This candidate selection process may comprise a comparison of one or more attributes of the new data records to the existing data records to determine which of the existing new data records are similar enough to the new data records to entail further comparison. A language-independent character-frequency based coding that can be used in candidate selection (bucketing) is described in the accompanying appendix which is part of this disclosure. The selected candidates may then undergo a more detailed comparison to the new records where a set of attributes are compared between the records to determine if an existing data record should be linked or associated with the new data record. This more detailed comparison may entail comparing each of the set of attributes of one record (e.g., an existing record) to the corresponding attribute in the other record (e.g., the new record) to generate a weight for that attribute. The weights for each of the set of attributes may then be summed to generate an overall weight which can then be compared to a threshold to determine if the two records should be linked.
Turning now to FIG. 4, a flow diagram for one such embodiment of a method for generating a weight from the comparison of attributes is depicted. Though the embodiment of the methodology depicted may be used to compare any two attributes (e.g., personal names, addresses, company names, etc.) it may especially useful in comparing business names, and will be described as such.
At step 410, two names are given or provided (e.g., input to a software application) such that these names may be compared. The names may each be in a standardized form comprising a set of tokens, as discussed above with respect to FIG. 3. An information score may be calculated for each of the names at step 420. This information score for the attribute may be the sum of the exact match values (also referred to as the information score for the token) of each of the tokens of the name. The information score of the two attributes may then be averaged at step 430.
Using an average value for the information score of the two attributes (instead of, for example, a minimum or maximum information score between the two attributes) may allow embodiments of the name comparison algorithm to allow the generated weight between two attributes to take into account missing tokens between the two attributes, and, in some embodiments, may allow the penalty imposed for missing tokens to be half the penalty imposed for that of a mismatch between two tokens. The information score of each of the tokens may, in turn, be based on the frequency of the occurrence of a token in a data sample. By utilizing relative frequency of tokens to determine an information score for the token, the commonality of certain tokens (e.g., “Inc.”) may be taken into account by scoring these tokens lower.
A weight between the two names can then be generated at step 440 by comparing the two names. This weight may then be normalized at step 450 to generate a final weight for the two names. In one embodiment, this normalization process may apply a scaling factor to the ratio of the generated weight to the average information score to generate a normalized index value. This normalized index value may then be used to index a table of values to generate a final weight.
It may be useful here to delve with more detail into the various steps of the embodiment of an algorithm for comparing names depicted in FIG. 4. As such, the first to be addressed will be the calculation of an average information score, as depicted in step 430. As discussed above, the information score for an attribute may be the sum of the exact match weights for each of the tokens of the attribute. It may be useful to describe embodiments of how these exact match weights are calculated. In one embodiment, an exact match weight table may have weight values for an exact match for a token or a default value to use for an exact match for a token. In other words, the exact match weight table may comprise a list of tokens with a corresponding weight value. This weight value may correspond to an exact match weight. Put a different way, if both tokens being compared are the same, the token may be located in the exact match weight table and the corresponding weight is used as the match weight for those two tokens. If two tokens are determined to be an exact match and the token is not in the exact match weight table, a default weight value may be utilized for the match weight.
In one embodiment, the weights associated with the tokens in the exact match weight table may be calculated from a sample set of data record, such as a set of data records associated with one or more of information sources 34, 36, 38 or a set of provided data records. Using the sample set of data records exact match weights may be computed using frequency data and match set data. The number of name strings (e.g., name attributes) NameTot in the sample set of data records may be computed, and for each name token T corresponding to these name strings a count: Tcount and a frequency Tfreq=Tcount/NameTot.
The tokens are then ordered by frequency with the highest frequency tokens first and a cumulative frequency for each token which is the sum of the frequencies for the token and all those that came before it is computed as depicted in Table 1 below:















Token
Freq
Cumulative Freq









T0
Tfreq-0
Tfreq-0



T1
Tfreq-1
Tfreq-0 + Tfreq-1



T2
Tfreq-2
Tfreq-0 + Tfreq-1 + Tfreq-2



. . .
. . .
. . .



TN
Tfreq-N
Tfreq-0 + ... + Tfreq-N










In some embodiments, all tokens up to and including the first token whose cumulative frequency exceeds 0.80 are then determined and for each of these tokens the exact match weight may be computed using the formula: ExactTi=−ln(Tfreq-i). If TM is the first token whose cumulative frequency exceeds 0.80 and TN is the last token or the lowest frequency token the default exact match weight can be computed by taking the average of −ln(Tfreq-M+1), . . . −ln(Tfreq-N). An embodiment of the compare algorithm described herein for comparing names may then be applied to a set of random pairs of names in the data set to generate. RanNameComp=The total number of name string pairs compared and For I=0 to MAX_SIM, RanSim-I=the total number of name string pairs whose normalized similarity is I. For each I, RanFreqSim-I=RanSim-I/RanNameComp can then be computed. MatchFreqSim-I=MatchSim-I/MatchNameComp can also be computed for a token using the weight generation process as described in U.S. patent application Ser. No. 11/521,928, entitled “METHOD AND SYSTEM FOR COMPARING ATTRIBUTES SUCH AS BUSINESS NAMES” by Norm Adams et al., filed Sep. 15, 2006, or U.S. patent application Ser. No. 11/522,223, entitled “METHOD AND SYSTEM FOR COMPARING ATTRIBUTES SUCH AS PERSONAL NAMES” by Norm Adams et al., filed Sep. 15, 2006, both of which are fully incorporated herein by reference. Final weights for a token may then be computed as: Weight-Norm-SimI=log 10(MatchFreqSim-I/RanFreqSim-I).
Once the exact match weights for a set of tokens are calculated they may be stored in a table in a database associated with Identity Hub 32. For example, the following pseudo code depicts one embodiment for calculating an information score for an attribute utilizing two tables an “initialContent” table comprising exact match weights for initials, and “exactContent” comprising exact match weights for other tokens:














tokenListInfo(tList)



  totalInfo = 0.0



  for token in tList:



    if token is an initial:



      totalInfo += initialContent(token)



    else



      totalInfo += exactContent(token)



  return totalInfo









Referring still to FIG. 4, once information scores are calculated and these weights averaged at step 430, a weight may be generated for the two names at step 440. Turning now to FIG. 5A, a flow diagram for one embodiment of a method for generating a weight between two attributes is depicted. More particularly, each token of one attribute may be compared at step 515 to each token of the other attribute. This comparison may take place according to the order of the set of tokens comprising each attributes. In other words, the first token of one attribute may be compared to each of the tokens of the other attribute, after which the second token of the attribute may be compared to each of the tokens of the other attribute, etc.
For each of these pairs of tokens it may be determined if a match exists between the two tokens at step 525. If no match exists between the two tokens at step 525 the current match weight may be set to zero at step 537. If a match exists between the two tokens, however, the current match weight for the two tokens may be calculated at step 535.
Once it has been determined if a match exists between the two tokens at step 525 and the match weight calculated at step 535 for the current match weight if such a match exists, it may be determined if a distance penalty should be imposed at step 547. In one embodiment, it may be determined if a distance penalty should be imposed, and the distance penalty computed, based on where the last match between a pair of tokens of the attributes occurred. To this end, a last match position may be determined at step 545 indicating where the last match between two tokens of the attributes occurred. If the difference in position (e.g., relative to the attributes) between the current two tokens being compared and the last match position is greater than a certain threshold a distance penalty may be calculated at step 555 and the current match weight adjusted at step 557 by subtracting the distance penalty from the current match weight. It will be apparent that these difference penalties may differ based upon the difference between the last match position and the position of the current tokens.
Match weights for previous tokens of the attributes may also be determined at steps 565, 567 and 575. More particularly, at step 565, a first previous match weight is determined for the token of one attribute currently being compared and the previous (e.g., preceding the current token being compared in order) token of the second attribute currently being compared, if it exists. Similarly, at step 567 a second previous match weight is determined for the token of second attribute currently being compared and the previous token of the first attribute currently being compared, if it exists. At step 575, a third previous match weight is determined using the previous tokens of each of the current attributes, if either token exist. The current match weight for the pair of tokens currently being compared may then be adjusted at step 577 by adding the third previous match weight to the current match weight.
The current match weight may then be compared to the first and second previous match weight at step 585, and if the current match weight is greater or equal to either of the previous match weights the weight may be set to the current match weight at step 587. If, however, the first or second previous match weight is greater than the current match weight the weight will be set to the greater of the first or second previous match weights at step 595. In this manner, after each of the tokens of the two attributes has been compared a weight will be produced.
It will be apparent that many types of data elements or data structures may be useful in implementing certain embodiments of the present invention. For example, FIG. 5B depicts a flow diagram for one embodiment of a method for generating a weight between two attributes utilizing a table. At step 510 a table may be built to aid in the comparison of the two names. This table may comprise a row for each of the tokens in one of the names plus an additional row, and a column for each of the tokens in the other name plus an additional column. Thus, the first row and the first column of the table may correspond to initial conditions, while each of the other cells of the table may correspond to a unique pair of tokens, one token from each of the names being compared. Each cell of the table may have the ability to store a position (e.g., cell) indicator and a weight. While a table is utilized in the embodiment illustrated it will be apparent that a table is an example structure only, and any data structure, structure storage may be utilized (e.g., an array of any dimension, a linked list, a tree, etc.).
After the table is built at step 510, it may be initialized at step 520 such that certain initial cells within the table have initial values. More particularly, in one embodiment each of the first row and first column may be initialized such that the position indicator may receive a null or zero value and the weight associated with each of these cells may be initialized to a zero value.
Each of the other cells (e.g., besides the initial cells) of the table may then be iterated through to determine a position and a value to be associated with the cell. For each cell it is determined if the cell has already been matched through an acronym match at step 530, and if so the cell may be skipped. If the cell has not been previously matched, however, at step 540 it may be determined if a match exists between the two tokens corresponding cell, if no match exists it may then be determined if either of the tokens corresponding to the cell is an acronym for a set of the tokens in the other name at step 532, by, in one embodiment, comparing the characters of one token to the first characters of a set of tokens of the other name. If one of the tokens is an acronym for a set of tokens in the other name, a last position indicator and cell weight (as described in more detail below) are calculated at step 534 for the set of cells whose corresponding tokens are the acronym and the set of tokens of the other name which correspond to the acronym. Pseudocode for determining if one token is an acronym for a set of tokens of the other name is as follows:












MAX_INIT_MATCH is the maximum acronym length, which, in this 


embodiment, is 3.


acroynmCheck(acToken, //a token which we will check as a possible 


acronym


 tokenList, //the other list of tokens


 currentPosition //start at this position to check for an acronym match


   )


 if (length(acTokan) < 2 or >MAX_INIT_MATCH)


   return NO_MATCH


 if (currentPosition + length(acToken) − 1 > length(tokenList))


   return NO_MATCH


 listPosition = currentPosition


 tokenPosition = 0


 totalInfo = 0


 while(tokenPosition != end of word)


   if firstChar of tokenList[listPosition] != acToken[tokenPosition]


     return NO_MATCH


   totalInfo = totalInfo + initialContent(firstChar) - INIT_PENALTY


   tokenPosition++


   listPosition++


 return MATCH, totalInfo, listPosition









If it is determined that neither of the tokens is an acronym at step 532, the match weight for the current cell may be set to zero at step 542. Returning to step 540, if a match exists between the two tokens corresponding to the current cell, the match weight for the two tokens may be calculated at step 542. Though virtually any type of comparison may be utilized to compare the two corresponding tokens and generate an associated match weight according to steps 540 and 542, in one embodiment it may be determined if an exact match, an initial match, a phonetic match, a nickname match or a nickname-phonetic match occurs and a corresponding match weight calculated as described in the aforementioned U.S. patent application Ser. No. 11/522,223, entitled “METHOD AND SYSTEM FOR COMPARING ATTRIBUTES SUCH AS PERSONAL NAMES” by Norm Adams et al., filed Sep. 15, 2006, which is fully incorporated herein by reference. Pseudo code for comparing two tokens and generating an associated match weight is as follows:












tokenCompare(t1, t2) //t1 and t2 are tokens.


  If one or both is an intial:


    If first chars agree:


      Return min(exactContent1, exactContent2) - Initial_Penalty


    Else


      Return 0.0, NO_MATCH


  Dist = normalizedEditDistance(t1, t2)


  If Dist == 0


    Return exactContent(t1), EXACT_MATCH


  If tokens match phonetically


    Return min(exactContent1, exactContent2) - phonetic_Penalty,


  PARTIAL_MATCH


  If tokens match via a nickname


    Return Return min(exactContent1, exactContent2) -


    Nickname_Penalty,


  PARTIAL_MATCH


  if there is a nickname-phonetic match


    Return Return min(exactContent1, exactContent2) -


    Nickphone_Penalty,


  PARTIAL_MATCH


  If Dist <= 0.2 * maximum token length


    Return min(exactContent1, exactContent2) - Nickname_Penalty,


  PARTIAL_MATCH


  Else:


    Return 0.0, NO_MATCH









Looking still at FIG. 5B, once it has been determined if a match exists between the two tokens corresponding to the cell at step 540 and the match weight calculated at step 542 if such a match exists, it may be determined if a distance penalty should be imposed at step 550 and the distance penalty calculated at step 552. In one embodiment, it may be determined if a distance penalty should be imposed, and the distance penalty computed, based on a difference between the position of a cell which corresponds to a last position match and the current cell. A difference in row position and column position may be calculated, and if the difference is greater than one (indicating that a distance penalty should be imposed), the largest of these differences may used to determine a distance penalty to impose. For example, if the difference between the current cells row and the row of the cell with the last match is two and the difference between the current cells column and the column of the cell with the last match is three, a distance penalty associated with the distance of three which may be utilized. It will be apparent that larger difference penalties may associated and imposed based upon a greater distance between the last match cell and the current cell being evaluated. If, indeed, it is determined that a distance penalty should be imposed and the distance penalty calculated at step 550, the match weight may then be adjusted at step 560 by subtracting the distance penalty from the match weight.
Using the match weight for the cell then, a cell weight and last match position for the cell may be calculated at step 570. A flow diagram for one embodiment of a method for calculating a last match position and a cell weight for a cell is depicted in FIG. 6. Generally, the cell weight and last match position may be calculated based on the last match position and the cell weight of surrounding cells. In particular, a cell weight from one cell adjoining the current cell (e.g., the row number of the current cell minus one and the same column number) may be determined at step 610 and the cell weight from another cell adjoining the current cell (e.g., the column number of the current cell minus one and the same row number) may be determined at step 620. At step 630, a temporary cell weight may be determined by adding the cell weight of the adjoining cell on a diagonal (e.g., the row number of the current cell minus one and the column number of the current cell minus one) to the match weight calculated for the current cell (step 570).
The cell weights from the two adjoining cells and the temporary cell weight may be compared at step 640. If the temporary cell weight is greater than either of the cell weights of the adjoining cells, the last match position of the current cell is set to the position of the current cell at step 642 and the cell weight of the current cell is set to the temporary cell weight at step 644. If however, either of the cell weights exceeds the temporary cell weight, the greater of the two cell weights will be assigned as the cell weight of the current cell and the value of the last match position indicator of that cell (e.g., adjoining cell with higher cell weight) will be assigned as the last position indicator of the current cell at step 652 or step 654.
Returning now to FIG. 5B, after every cell in the table has been iterated through in the manner described above, at step 580 the weight for the two names being compared may be the cell weight of the last cell of the table (e.g., last row, last column). Once the weight is determined at step 580 then, this weight may, in one embodiment, be normalized as depicted in step 450 of FIG. 4, by computing a ratio of the weight for the two names to the average information value of the two names and multiplying this ratio by a maximum index value to yield a normalized index value. This normalized index value may then be used to determine a final weight by, in one embodiment, indexing a table of weights stored in a database of identity hub 32 using the index value.
Before delving into examples of the application of embodiments of the above described methods, it may be useful to discuss how various match and distribution penalties are determined. In one embodiment, to calculate these penalties an embodiment of a compare algorithm such as that described above with respect to FIG. 4 is applied to a set of random pairs of names obtained from a sample set of data records. Typically around 10 million pairs of names may be used. While applying the compare algorithm the following may be tracked:

    
    
        RanComp=The total number of name tokens that are compared.
        RanExact=The total number of name tokens that agree exactly.
        RanInitial=The total number of name tokens that agree via an initial match. An initial match is one in which the first characters agree, one token has length 1 and the other's length is greater than 1.
        RanPhonetic=The total number of name tokens that agree phonetically, not exactly.
        RanNickname=The total number of name tokens that have a common nickname, and don't match Exactly.
        RanNickPhone=The total number of name tokens that have nicknames that match phonetically and don't match Exactly or via a Nickname alone
        RanEdit=The total number of name tokens that match via an edit distance compare and do not match exactly.
        RanDist-0=(CXNM only) The total number of name tokens that match exactly and where the previous tokens in each name string also matched.
        
            Example 1: in BOBS FLOWER SHOP compared to BOBS FLOWER SHOP, the exact matches for FLOWER would be counted, since the there was an exact match BOBS in the previous tokens. Similarly SHOP would also get counted.
            Example 2: In BOBS FLOWER SHOP compared to BOBS PRETTY FLOWER SHOP. FLOWER would not get counted, since the previous tokens are BOBS and PRETTY and that is a match. SHOP would get counted.
        
        
        RanDist-1, RanDist-2, RanDist-3=(CXNM only) The total number of name tokens that match exactly and where the max number of extra tokens between the current match an the previous is 1, 2, or 3.
        
            In example 2 above, for the FLOWER match, the previous match is BOBS. There are no extra tokens in the first string and 1 in the second so, this would be an example of RanDist-1.
            If the strings, BOBS PRETTY FLOWERS and BOBS GOOD FLOWERS are compared, the distance for the exact match FLOWERS, would still be 1, since there is one extra token in each string.
            If the strings, BOBS PRETTY NICE FLOWERS and BOBS GOOD FLOWERS are compared, the distance for the exact match FLOWERS, would be 2, since there are 2 extra tokens in the first sting.
            If the number of extra tokens is greater than or equal to 3, it is counted in RanDist-3.
        
        
    
    


The following frequencies can then be computed:

    
    
        RanProbExact=RanExact/RanComp 
        RanProbInitial=RanInitial/RanComp 
        RanProbPhonetic=RanPhonetic/RanComp 
        RanProbNickname=RanNickname/RanComp 
        RanProbNickPhone=RanNickPhone/RanComp 
        RanProbEdit=RanEdit/RanComp 
        RanProbDist-0=RanDist-0/RanComp 
        RanProbDist-1=RanDist-1/RanComp 
        RanProbDist-2=RanDist-2/RanComp 
        RanProbDist-3=RanDist-3/RanComp 
    
    


Using the process described above in conjunction with generating exact match weights, a set of matched name pairs can be derived, and the following frequencies derived:

    
    
        MatchProbExact=MatchExact/MatchComp 
        MatchProbInitial=MatchInitial/MatchComp 
        MatchProbPhonetic=MatchPhonetic/MatchComp 
        MatchProbNickname=MatchNickname/MatchComp 
        MatchProbNickPhone=MatchNickPhone/MatchComp 
        MatchProbEdit=MatchEdit/MatchComp 
        MatchProbDist-0=MatchDist-0/MatchComp 
        MatchProbDist-1=MatchDist-1/MatchComp 
        MatchProbDist-2=MatchDist-2/MatchComp 
        MatchProbDist-3=MatchDist-3/MatchComp 
    
    


Using these frequencies the following marginal weights may be computed:

    
    
        MarginalExact=log10(MatchProbExact/RanProbeExact)
        MarginalInitial=log10(MatchProbInitial/RanProbeInitial)
        MarginalPhonetic=log10(MatchProbPhonetic/RanProbePhonetic)
        MarginalNickname=log10(MatchProbNickname/RanProbeNickname)
        MarginalNickPhone=log10(MatchProbNickPhone/RanProbeNickPhone)
        MarginalEdit=log10(MatchProbEdit/RanProbeEdit)
        MarginalDist-0=log10(MatchProbDist-0/RanProbeDist-0)
        MarginalDist-1=log10(MatchProbDist-1/RanProbeDist-1)
        MarginalDist-2=log10(MatchProbDist-2/RanProbeDist-2)
        MarginalDist-3=log10(MatchProbDist-3/RanProbeDist-3)
    
    


and the respective penalties computed as follows:

    
    
        Initial Penalty=MarginalExact−MarginalInitial 
        Initial Penalty=MarginalExact−MarginalInitial 
        Phonetic Penalty=MarginalExact−MarginalPhonetic 
        Nickname Penalty=MarginalExact−MarginalNickname 
        NickPhone Penalty=MarginalExact−MarginalNickPhone 
        Edit Distance Penalty=MarginalExact−MarginalEdit 
        DistPenalty1=MarginalDist-0−MarginalDist-1 
        DistPenalty2=MarginalDist-0−MarginalDist-2 
        DistPenalty3=MarginalDist-0−MarginalDist-3 
    
    


An example of the application of embodiments of the systems and methods disclosed herein to two actual names may now be illustrated with respect to the example table of FIG. 7 to further aid in the understanding of these systems and methods. Assume that it is desired to obtain a weight between two names: “Bobs Flower Shop” and “Bobs Very Pretty Flower Shoppe” and that the following parameters are to be used for the comparison, the exact match weights for each of the tokens is:
BOBS—200
VERY—150
PRETTY—300
FLOWER—400
SHOPPE—600
SHOP—150
While the distance penalty for a distance of 3 is 100 and a phonetic penalty is 100.
In accordance with one embodiment of the systems and methods of the present invention an average information score may be calculated for the two names being compared (step 430). In one embodiment, this is done using the exact match weights for each of the tokens in each of the names. According to this method, the information score for Bobs Flower Shop is 750 (e.g., 200+400+150) and the information score for the name “Bobs Very Pretty Flower Shoppe” is 1650 (200+150+300+400+600), making the average of the two information scores 1200.
Once an average information score for the two names is computed (step 430) a weight for the two names may be generated (step 440). In one embodiment, table 700 is constructed (step 510). Where each cell 702 has the ability to keep a position indicator (e.g., row, column) and a cell weight. Cells 702a of the table may then be initialized (step 520).
Once cells 702a of the table have been initialized, the remainder of the cells 702 of the table 700 may be iterated through. Starting with cell 702b (e.g., row 1, column 1), it is determined that a match occurs between the two tokens corresponding to the cell 702b (step 540). The match weight for these two tokens may then bq calculated (step 549), which in this case is 200. The cell weight values for adjoining cells may then be determined (steps 610, 620), and from this it can be determined that the cell weight (0) from the diagonal cell 702a1 plus 200 (e.g., temporary cell weight for the cell) is greater than the cell weight of either adjoining cell 702a2, 702a3 (step 640). Thus, the last match position indicator of cell 702b is set to the current cell 702b (11) and the cell weight of the current cell is set to the calculated match weight (200) (steps 642, 644).
The last match position indicator and cell weight for the next cell 702c may then be calculated. It is determined that no match occurs between the two tokens corresponding to the cell 702c (step 540). As no acronym match occurs (step 532) the match weight for this cell is then set to zero (step 542). A temporary cell weight may then be calculated (step 630) and compared to the cell weights of adjoining cells 702b, 702a4 (steps 640, 650) and from this it can be determined that the cell weight (100) from the adjoining cell 702b is greater than the cell weight of adjoining cell 702a4 or the cell weight of diagonal cell 702a3 plus the match weight for the current cell (0) (e.g., temporary cell weight). Thus, the last match position indicator of current cell 702c is set to the last match position indicator of adjoining cell 702b (1,1) and the cell weight of the current cell 702c is set to the cell weight of the adjoining cell 702b with the greater cell weight (step 652)
Similarly, cells 702d, 702e, 702f, 702g, 702h, 702i, 702j and 702k may be iterated through with similar results as those described above with respect to cell 702c. Upon reaching cell 702l, however, it may be determined that a match occurs between the two tokens corresponding to the cell 702l (step 540). The match weight for the tokens corresponding to cell 702l (e.g., “Flower” and “Flower”) may then be calculated (step 542), which in this case may be 400. It may then be determined if a distance penalty should be imposed by comparing the position of the last match position of the diagonal cell 702h with the position of the current cell 702l (step 550). This comparison may be accomplished by subtracting the row indices from one another (e.g., 4-1) and the column indices from one another (e.g., 2-1) and taking the maximum of these values (e.g., 3) and comparing this distance value to a threshold level to determine if a distance penalty should be imposed. In this case, the threshold value for a distance penalty may be a distance of one, as three is greater than one it may be determined that a distance penalty should be imposed. The distance penalty corresponding to the distance value (e.g., 3) may then be subtracted from the calculated match weight for the current cell (steps 552, 560). In this case, the distance penalty is 100, which may be subtracted from the match weight of 400 to adjust the match weight of cell 702l to 300. The cell weight values for adjoining cells may then be determined, and from this it can be determined that the cell weight (200) from the diagonal cell 702h plus the match weight for the current cell 702l (e.g., 300) is greater than the cell weight of either adjoining cell 702k, 702i (e.g., 200 and 200 respectively) (step 640). Thus, the last match position indicator of cell 702l is set to the current cell 702l (4,2) and the cell weight of the current cell 702l is set to the calculated match weight plus the cell weight from the diagonal cell 702h (e.g., 300+200=500) (steps 642, 644).
The last position match indicator and cell weights for cells 702m, 702n and 702o may be calculated similarly to the calculations described above. Upon reaching cell 702p, however, it may be determined that a match occurs between the two tokens corresponding to the cell 702p (step 540). The match weight for the tokens corresponding to cell 702p (e.g., “Shoppe” and “Shop”) may then be calculated (step 542), which in this case may be 50 (as the match between “Shoppe” and “Shop” may be a phonetic match its weight may be the minimum of the exact match weights for Shoppe and Shop minus the phonetic penalty weight). It may then be determined if a distance penalty should be imposed by comparing the position of the last match position of the diagonal cell 702l with the position of the current cell 702p (step 550). This comparison may be accomplished by subtracting the row indices from one another (e.g., 5-4) and the column indices from one another (3-2) and taking the maximum of these values (e.g., 1) and comparing this distance value to a threshold level to determine if a distance penalty should be imposed. In this case the threshold value for a distance penalty may be a distance of one and as such a distance penalty should not be imposed. Thus, the match weight of the current cell 702p is 50. The cell weight values for adjoining cells 702o, 702m may then be determined (steps 610, 620), and from this it can be determined that the cell weight from the diagonal cell 702l (500) plus the match weight for the current cell 702p (50) is greater than the cell weight of either adjoining cell 702k, 702i (e.g., 500 and 500 respectively) (step 640). Thus, the last match position indicator of cell 702p is set to the current cell 702p (5,3) and the cell weight of the current cell 702p is set to the calculated match weight plus the cell weight from the diagonal cell 702l (e.g., 500+50=550) (steps 642, 644).
Reading the last cell 702p of table 700, it can be determined that the weight for the two names being compared is 550. This weight may then be normalized according to a maximum similarity index and a ratio of the weight to an average information score for the two names (step 450). For example, if the maximum similarity index is 10, the weight may be normalized to a normalized index value of 4 by rounding the results of the equation 10*550/1200. This normalized index value may be used as index into a normalized weight table to generate the final weight for the two tokens (step 450). For example, the normalized weight of 4 may index to a final weight of 441 for the two names.
To further aid in an understanding of embodiments of the systems and methods of the present invention it may be useful to illustrate an example of the application of embodiments of the systems and methods of the present invention to a comparison of names where an acronym is present in one of the two names being compared. A table for use in illustrating such an example is depicted in FIG. 8. Assume that it is desired to obtain a weight between two names: “Bobs VP Flower Shop” and “Bobs Very Pretty Flower Shop” and that the following parameters are to be used for the comparison, the exact match weights for each of the tokens is:
BOBS—200
VERY—150
PRETTY—300
FLOWER—400
SHOP—150
While the distance penalty for a distance of 3 is 100.
In one embodiment, an average information score may be calculated for the two names being compared (step 430). In one embodiment, this is done using the exact match weights for each of the tokens in each of the names. According to this method, the information score for Bobs VP Flower Shop is 1050 (e.g., 200+300+400+150) and the information score for the name “Bobs Very Pretty Flower Shop” is 1200 (200+150+300+400+150), making the average of the two information scores 1125.
Once cells 802a of the table have been initialized, the remainder of the cells 802 of the table 800 may be iterated through. Starting with cell 802b (e.g., row 1, column 1), it is determined that a match occurs between the two tokens corresponding to the cell 802b (step 540). The match weight for these two tokens may then be calculated (step 542), which in this case is 200. The cell weight values for adjoining cells may then be determined (steps 610, 620), and from this it can be determined that the cell weight (0) from the diagonal cell 802a1 plus 200 (e.g., temporary cell weight for the cell) is greater than the cell weight of either adjoining cell 802a2, 802a3 (step 640). Thus, the last match position indicator of cell 802b is set to the current cell 802b (1,1) and the cell weight of the current cell 802b is set to the calculated match weight (200) (steps 642, 644).
Cells 802c-802f may similarly be iterated through, as discussed above. Upon reaching cell 802g it may be determined that no match exists between the two tokens corresponding to cell 802g (step 540), however, it may be determined that VP is acronym (step 532). This determination may be accomplished by comparing the first character of a first token “VP” corresponding to cell 802g (e.g., “V”) to the first character of the other token corresponding to cell 802g (e.g., very). As the character “V” matches the first character of the token “Very”, the next character of the token “VP” (e.g., “P”) is compared to the following token in the other name (e.g., “Pretty”) as these characters match, and there are no more characters of the first token (e.g., “VP”), it can be determined that the token “VP”, is an acronym and values can be computed for the set of cells 802g, 802k corresponding to the acronym token (e.g., each cell which corresponds to one character of the acronym token and a token of the other name) similarly to the computation discussed above (in the example depicted with respect to FIG. 8A, both the matches between the characters of the acronym (e.g., “V” and “P”) and their respective matching tokens (e.g., “Very” and “Pretty”) generate an initial match with a weight of 50). After values are computer for the cells 802g and 802k the table may resemble that depicted in FIG. 8A.
The rest of the cells 802 of table 800 may then be iterated through beginning with cell 802d to calculate last position matches and cell weights for these cells as described above. Cells 802g and 802k may be skipped during this iterative process as these cells have already been matched via an acronym (step 530). After iterating through the remainder of cells 802 of table 800, table 800 may resemble the table depicted in FIG. 8B.
Reading the last cell 802u of table 800 it can be determined that the weight for the two names being compared is 850. This weight may then be normalized according to a maximum similarity index and a ratio of the weight to an average information score for the two names (step 450). For example, if the maximum similarity index is 10, the weight may be normalized to a normalized index value of 8 by rounding the results of the equation 10*850/1125. This normalized index value may be used as index into a normalized weight table to generate the final weight for the two tokens (step 450). For example, the normalized weight of 8 may index to a final weight of 520 for the two names.
Pseudo code describing one embodiment of a method for comparing names is presented below to further help in the comprehension of embodiments of the present invention:












Compare(tList1, tList2) // tList1 and tlist2 are lists of tokens


L1 = len(tList1)


L2 = len(tList2)


compareTable = a 2 dim-array indexed 0...L1 and 0...L2 containing real 


numbers.


Set all entries in row 0 to 0.0 and None


Set all entries in column 0 to 0.0 and None


For r in range(1...L1)


 For c in range(1..L2)


  tokenInfo, compareResult = compare(tList1[r], tList2[c])


  if the compareResult was a match (exact or partial):


   get I_MATCH and J_MATCH from compareTable[r−1][c−1]


   maxDiff = max(r − I_MATCH, c − J_MATCH)


   if maxDiff > 1:


     //need to apply the distance penalty.


    penalty = posPenalty[maxDiff]


   else


    penalty = 0.0


   diagonalContent =


       compareTable[r−1][c−1].infoScore+tokenInfo −


penalty


    compareTable[r][c]. infoScore = max(compareTable[r−


1][c].infoScore,


      compareTable[r][c−1]. infoScore,


        compareTable[r][c]. infoScore) + tokenInfo)


    if new infoScore is result of new match:


     compareTable[r][c].I_MATCH = r


     compareTable[r][c].J_MATCH = c


    elsif new info score comes from [r, c−1] or [r−1, c]


     compare I_MATCH, J_MATCH from that cell


   else if acronymCheck(tList1[r], tList2, c) or


acronymCheck(tList2[c], tList1, c)


    L = length of acronym


    for i in range(0, L−1)


     compareTable[r+i, c+i].I_Match = r+L


     compareTable[r+i,c+i].J_Match = c+L


     compareTable[r+i,c+i].infoScore = max(compareTable[r−


1][c].infoScore,


        compareTable[r][c−1]. infoScore,


         compareTable[r][c]. infoScore) +


acronymScore


   else: //compare results was not a match


    compareTable[r][c].infoScore = max(


        compareTable[r−1][c−1]].infoScore,


        compareTable[r][c−1]].infoScore,


         compareTable[r][c]].infoScore)


    update compareTable[r][c].I_MATCH and J_MATCH


using contents of appropriate cell: either [r−1, c−1], [r, c−1] or [r−1, c].


maxInfo = max(tokenListInfo(tList1), tokenListInfo(tList2))


normalizedInfo = 10 * round(compareTable[L1][L2]/maxInfo)


return normalizedInfo









The normalized index value which may be returned by the embodiment of the present invention described in the pseudo code above may be used to index a table of values to obtain a final weight, as described above. Such a table may resemble the following, where the maximum index value may be 16:














NORM_ADJWGT_0|−185|



NORM_ADJWGT_1|−101|



NORM_ADJWGT_2|−53|



NORM_ADJWGT_3|−12|



NORM_ADJWGT_4|37|



NORM_ADJWGT_5|69|



NORM_ADJWGT_6|142|



NORM_ADJWGT_7|207|



NORM_ADJWGT_8|261|



NORM_ADJWGT_9|280|



NORM_ADJWGT_10|277|



NORM_ADJWGT_11|309|



NORM_ADJWGT_12|360|



NORM_ADJWGT_13|452|



NORM_ADJWGT_14|477|



NORM_ADJWGT_15|564|



NORM_ADJWGT_16|598|









In the foregoing specification, the invention has been described with reference to specific embodiments. However, it should be understood that the description is by way of example only and is not to be construed in a limiting sense. It is to be further understood, therefore, that numerous changes in the details of the embodiments of this invention and additional embodiments of this invention will be apparent to, and may be made by, persons of ordinary skill in the art having reference to this description. It is contemplated that all such changes and additional embodiments are within the scope of the invention as detailed in the following claims.

## Claims

1. A computer-implemented method for comparing a first data record and a second data record, wherein the first and second data records are located in one or more data sources, the first data record comprises a first attribute and the second data record comprises a second attribute, the method comprising:
parsing the first and second attributes to produce a set of tokens for each of those attributes, wherein the data sources employ at least two different languages and at least one of the first and second attributes is expressed in a language employing other than a Latin alphabet;
calculating an average information score for the first attribute and the second attribute, wherein the average information score is calculated based upon a matching of tokens for each of the first and second attributes;
generating a weight for the first attribute and the second attribute; and
normalizing the weight based on the average information score;
wherein generating the weight comprises comparing each of a set of tokens of the first attribute to each of a set of tokens of the second attribute such that pairs of tokens are compared, and comparing each pair of tokens comprises:
determining a current match weight for a pair of tokens;
determining a first previous match weight corresponding to the pair of tokens;
determining a second previous match weight corresponding to the pair of tokens;
setting the weight to the current match weight in response to the current match weight being greater than the first previous match weight or the second previous match weight; and
setting the weight to the greater of the first previous match weight or the second previous match weight in response to either the first previous match weight or the second previous match weight being greater than the current match weight; and

linking the first data record and the second data record based on the normalized weight between the two attributes.

2. The method of claim 1, wherein the pairs of tokens are compared in an order of the set of tokens of the first attribute.

3. The method of claim 2, wherein comparing each pair of tokens further comprises determining whether a first token or a second token of the pair of tokens is an acronym.

4. The method of claim 3, wherein comparing each pair of tokens further comprises determining a set of pairs of tokens corresponding to the acronym in response to the first token or the second token being determined as an acronym and comparing each of the set of pairs before comparing any other pair of tokens.

5. The method of claim 4, wherein determining a current match weight for each comparison of a pair of tokens comprises:
determining whether there is a match between the pair of tokens;
determining a current match weight in response to a determination that there is a match between the pair of tokens;
setting the current match weight to zero in response to a determination that there is not a match between the pair of tokens; and
adjusting the current match weight for the pair of tokens by a third previous match weight.

6. The method of claim 5, wherein the first previous match weight is generated based on a first previous token corresponding to a first token of the pair of tokens.

7. The method of claim 6, wherein the second previous match weight is generated based on a second previous token corresponding to a second token of the pair of tokens.

8. The method of claim 7, wherein determining whether a match exists comprises determining whether there is an exact match, an initial match, a phonetic match, a nickname match, a nickname-phonetic match or an edit distance match.

9. The method of claim 8, wherein determining a current match weight for each comparison of a pair of tokens further comprises:
determining a first exact match weight for a first token of the pair of tokens;
using the first exact match weight as the current match weight in response to a determination of an exact match between the pair of tokens; and
in response to a determination of an initial match, a phonetic match or an edit distance match between the pair of tokens, determining the second exact match weight for the second token of the pair, taking the lesser of the first exact match weight and the second exact match weight as an initial match weight and applying a penalty to the initial match weight to generate the current match weight.

10. The method of claim 9, wherein determining a current match weight further comprises determining whether to apply a distance penalty to the current match weight.

11. The method of claim 10, wherein the determination of whether to apply a distance penalty is based on a difference between a last match position and a position of the current tokens.

12. The method of claim 11, wherein the distance penalty is based on the difference.

13. A computer program product for comparing a first data record and a second data record, wherein the first and second data records are located in one or more data sources, the first data record comprises a first attribute and the second data record comprises a second attribute, the computer program product comprising:
a computer readable storage medium having computer readable program code embodied therewith, the computer readable program code configured to:
parse the first and second attributes to produce a set of tokens for each of those attributes, wherein the data sources employ at least two different languages and at least one of the first and second attributes is expressed in a language employing other than a Latin alphabet;
calculate an average information score for the first attribute and the second attribute, wherein the average information score is calculated based upon a matching of tokens for each of the first and second attributes;
generate a weight for the first attribute and the second attribute; and
normalize the weight based on the average information score;
wherein generating the weight comprises comparing each of a set of tokens of the first attribute to each of a set of tokens of the second attribute such that pair of tokens are compared, and comparing each pair of tokens comprises:
determining a current match weight for a pair of tokens;
determining a first previous match weight corresponding to the pair of tokens;
determining a second previous match weight corresponding to the pair of tokens;
setting the weight to the current match weight in response to the current match weight being greater than the first previous match weight or the second previous match weight; and
setting the weight to the greater of the first previous match weight or the second previous match weight in response to either the first previous match weight or the second previous match weight being greater than the current match weight; and

link the first data record and the second data record based on the normalized weight between the two attributes.

14. The computer program product of claim 13, wherein the pairs of tokens are compared in an order of the set of tokens of the first attribute.

15. The computer program product of claim 14, wherein comparing each pair of tokens further comprises determining whether a first token or a second token of the pair of tokens is an acronym.

16. The computer program product of claim 15, wherein comparing each pair of tokens further comprises determining a set of pairs of tokens corresponding to the acronym in response to the first token or the second token being determined as an acronym and comparing each of the set of pairs before comparing any other pair of tokens.

17. The computer program product of claim 16, wherein determining a current match weight for each comparison of a pair of tokens comprises:
determining whether there is a match between the pair of tokens;
determining a current match weight in response to a determination that there is a match between the pair of tokens;
setting the current match weight to zero in response to a determination that there is not a match between the pair of tokens; and
adjusting the current match weight for the pair of tokens by a third previous match weight.

18. The computer program product of claim 17, wherein the first previous match weight is generated based on a first previous token corresponding to a first token of the pair of tokens.

19. The computer program product of claim 18, wherein the second previous match weight is generated based on a second previous token corresponding to a second token of the pair of tokens.

20. The computer program product of claim 19, wherein determining if a match exists comprises determining whether there is an exact match, an initial match, a phonetic match, a nickname match, a nickname-phonetic match or an edit distance match.

21. The computer program product of claim 20, wherein determining a current match weight for each comparison of a pair of tokens further comprises:
determining a first exact match weight for a first token of the pair of tokens;
using the first exact match weight as the current match weight in response to a determination of an exact match between the pair of tokens; and
in response to a determination of an initial match, a phonetic match or an edit distance match between the pair of tokens, determining a second exact match weight for the second token of the pair, taking the lesser of the first exact match weight and the second exact match weight as an initial match weight and applying a penalty to the initial match weight to generate the current match weight.

22. The computer program product of claim 21, wherein determining a current match weight further comprises determining whether to apply a distance penalty to the current match weight.

23. The computer program product of claim 22, wherein the determination of whether to apply a distance penalty is based on a difference between a last match position and a position of the current tokens.

24. A system for comparing data records, the system comprising:
at least one data source comprising a first data record and a second data record, wherein the first data record comprises a first attribute and the second data record comprises a second attribute, and wherein the at least one data source employs at least two different languages; and
a hub coupled with the at least one data source, the hub comprising a processor configured with logic to:
parse the first and second attributes to produce a set of tokens for each of those attributes, wherein at least one of the first and second attributes is expressed in a language employing other than a Latin alphabet;
calculate an average information score for the first attribute and the second attribute, wherein the average information score is calculated based upon a matching of tokens for each of the first and second attributes;
generate a weight for the first attribute and the second attribute; and
normalize the weight based on the average information score;
wherein generating the weight comprises comparing each of a set of tokens of the first attribute to each of a set of tokens of the second attribute such that pair of tokens are compared, and comparing each pair of tokens comprises:
determining a current match weight for a pair of tokens;
determining a first previous match weight corresponding to the pair of tokens;
determining a second previous match weight corresponding to the pair of tokens;
setting the weight to the current match weight in response to the current match weight being greater than the first previous match weight or the second previous match weight; and
setting the weight to the greater of the first previous match weight or the second previous match weight in response to either the first previous match weight or the second previous match weight being greater than the current match weight; and

link the first data record and the second data record based on the normalized weight between the two attributes.