1. A system for assessing similarity between character strings, the system comprising: a data collection to store a collection of character strings; and a server to access the data collection, the server comprising a processor configured with logic to: calculate an initial similarity score for a first character string and a second character string based on an edit distance algorithm; identify the first character string and the second character string as candidate similar character strings from the data collection based on the calculated initial similarity score being greater than or equal to a similarity threshold value; determine, when the first character string and the second character string are identified as similar character strings, a frequency of occurrence for at least one of the first character string and the second character string from the collection of character strings, wherein the frequency of occurrence comprises a total number of times that at least one of the first character string and the second character string is present in the collection of character strings; and decrease an occurrence of false designations of character strings as being similar, the decreasing further comprising: adjusting the initial similarity score to a greater value as a final similarity score when the determined frequency of occurrence is no greater than a low frequency threshold value, adjusting the initial similarity score to a lower value as the final similarity score when the frequency of occurrence is greater than a high frequency threshold value, and designating the first character string and the second character string as similar based on the final similarity score being greater than or equal to the similarity threshold value.
1. A system for assessing similarity between character strings, the system comprising: a data collection to store a collection of character strings; and a server to access the data collection, the server comprising a processor configured with logic to: calculate an initial similarity score for a first character string and a second character string based on an edit distance algorithm; identify the first character string and the second character string as candidate similar character strings from the data collection based on the calculated initial similarity score being greater than or equal to a similarity threshold value; determine, when the first character string and the second character string are identified as similar character strings, a frequency of occurrence for at least one of the first character string and the second character string from the collection of character strings, wherein the frequency of occurrence comprises a total number of times that at least one of the first character string and the second character string is present in the collection of character strings; and decrease an occurrence of false designations of character strings as being similar, the decreasing further comprising: adjusting the initial similarity score to a greater value as a final similarity score when the determined frequency of occurrence is no greater than a low frequency threshold value, adjusting the initial similarity score to a lower value as the final similarity score when the frequency of occurrence is greater than a high frequency threshold value, and designating the first character string and the second character string as similar based on the final similarity score being greater than or equal to the similarity threshold value. 8. The system of claim 1 , wherein the collection of character strings stores a collection of people names such that the first and second character strings represent names of people.