Best Approach To Match Phrase Queries

March 11, 2016 6:27 pm0 commentsViews: 40

The process of queries to find the match between phrases and sentences in a database is termed by Fuzzy Matching. It is a computer based application which helps to assist different translation and computational algorithm. It is applied in case there no matching is found between phrases or sentences. It is always targeted to find the developed match. But the point is that it never meets the hundred percent matches. The level of threshold limit of matching is primarily settled by the application. Data orientation consultants give the formulae to match the respective logic, defined by the membership function. The membership possibility for the typical set always lies between zeros to one. It can also be defined by the term true and false, the degrees of manipulation to solve the match of variant phrases. We can deal with a specific example to make the idea very clear. For illustration, you can take a person as a sample who gets 95 % similar score, whereas, the same person may have the possibility to receive the 75 % of the score. Except true or false the dimension of degree, manipulation is also be compared with this logic. The total process of comparing the data of various lengths has been introduced to identify the non similar matches. It is the study of the mathematics branch relating artificial intelligence. It also deals with the control system in engineering stream. The computer algorithms are designed based on the fuzzy logic system. Depending on various matches of similarity and non similarity, the algorithms are derived.

Match Phrase Queries

This mathematics strategy we can also implant in the formulation of a business solution. The profit, the amount of investments, the outputs, and the customers’ facility towards business involvement are the various factors can be disciplined by the improvisation of Fuzzy Matching. In case, any company determines a number of target customers and make a respective prospectus by providing them different identification. Suppose one customer may sign by *1. In terms of making the duplication of the list may happen that the same person may sign by *11 or by *111. This is called misspelling. Yet another term types also define the same characteristics. The point to be noted is that all different symbolised personalities are actually the same one. If we take the same example in consideration using advanced fuzzy logic, that may signify that the different numbers in the duplicate list of customers may have a different name, but in practical they are the same person. This process has the applications in business industry to find the exact matching or displacement between spelling difference and input phrases. It has the provisions to search the databases, based on the translation that has been stored in the memory. It works by matching the similarity percentage. This technology is unable to replace human beings in case of translating the language. But with the advent of research and growth in artificial intelligence, the process may be able to replace humans in early future with near about 100 percent perfection.

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