1. A method for mapping automotive data, comprising: at a server computer implementing a natural language processing system, determining whether a source string is present in a target string, wherein the source string and the target string represent text strings each comprising a sequence of characters that identify a vehicle characteristic, wherein the source string is from a first source communicatively connected to the server computer over a first network connection, wherein the target string is from a second source communicatively connected to the server computer over a second network connection, and wherein the first source and the second source employ different naming conventions for automotive data collected thereby; if the source string is present in the target string, outputting a match containing the source string and the target string; and if the source string is not present in the target string, computing a similarity factor based at least on a first set of parameters, wherein the first set of parameters comprises a Levenshtein edit distance parameter based on a comparison of characters in the source string and the target string, a single-word matches parameter based on a comparison of words in the source string and the target string, and a multi-word matches parameter based on a comparison of successive words in the source string and the target string, the similarity factor representing a measure of similarity between the source string and the target string; wherein the step of determining whether the source string is present in the target string further comprises: determining if an automotive base term that matches the source string is present in an automotive lexicon; utilizing the automotive base term to create a set of strings that includes one or more terms in the automotive lexicon; and comparing each string in the set of strings with the target string; utilizing the match to find from the first source, the second source, or both, pricing information associated with multiple vehicles having a user-selected trim; wherein the pricing information comprises at least one trade-in price, at least one sale price, at least one list price, or a combination thereof; wherein utilizing the match to find pricing information associated with the vehicles having the user-selected trim comprises aggregating sales information from the first and second sources according to the source string, and determining a value of the vehicles having the user-selected trim based on the aggregated sales information; wherein at least a portion of the sales information is aggregated in real time in response to a user selecting the vehicles having the user-selected trim; and wherein the determined value of the vehicles having the user-selected trim is presented to the user and is dynamically updated to reflect at least the portion of the sales information aggregated in real time.
1. A method for mapping automotive data, comprising: at a server computer implementing a natural language processing system, determining whether a source string is present in a target string, wherein the source string and the target string represent text strings each comprising a sequence of characters that identify a vehicle characteristic, wherein the source string is from a first source communicatively connected to the server computer over a first network connection, wherein the target string is from a second source communicatively connected to the server computer over a second network connection, and wherein the first source and the second source employ different naming conventions for automotive data collected thereby; if the source string is present in the target string, outputting a match containing the source string and the target string; and if the source string is not present in the target string, computing a similarity factor based at least on a first set of parameters, wherein the first set of parameters comprises a Levenshtein edit distance parameter based on a comparison of characters in the source string and the target string, a single-word matches parameter based on a comparison of words in the source string and the target string, and a multi-word matches parameter based on a comparison of successive words in the source string and the target string, the similarity factor representing a measure of similarity between the source string and the target string; wherein the step of determining whether the source string is present in the target string further comprises: determining if an automotive base term that matches the source string is present in an automotive lexicon; utilizing the automotive base term to create a set of strings that includes one or more terms in the automotive lexicon; and comparing each string in the set of strings with the target string; utilizing the match to find from the first source, the second source, or both, pricing information associated with multiple vehicles having a user-selected trim; wherein the pricing information comprises at least one trade-in price, at least one sale price, at least one list price, or a combination thereof; wherein utilizing the match to find pricing information associated with the vehicles having the user-selected trim comprises aggregating sales information from the first and second sources according to the source string, and determining a value of the vehicles having the user-selected trim based on the aggregated sales information; wherein at least a portion of the sales information is aggregated in real time in response to a user selecting the vehicles having the user-selected trim; and wherein the determined value of the vehicles having the user-selected trim is presented to the user and is dynamically updated to reflect at least the portion of the sales information aggregated in real time. 6. The method of claim 1 , wherein the single-word matches parameter is computed by: dividing a number of single words present in both the source string and the target string by a total number of words in the target string.