i have tried what you said to me here pattern analysis
but i think it isnt working
This is my workflow:
TradeFinance 2.knwf (78.5 KB) (This is the reseted workflow because the file was to large)
For the blue workflow I have used TM full file Oct 18 1.xlsx (1.3 MB)
For the Yellow part of the workflow i have used this file KNIME_CountryNames.xlsx (14.5 KB). The yellow area serves to assign the correct name to the country names existing in the blue area and possibly misspelled. if, for example, Thailand was written as “Tailand” in the blue area, reference should be made to the list added in the yellow area, since it contains the correct name, i.e. “Thailand”. Tailand is to become Thailand.
Do you know how to go on with this? I have tried the similarity search but it doesnt give me the new Name.
Have you considered Soundex for matching? I think this will need some Python or Java but libraries exist to do the heavy lifting.
Obviously really bad spelling errors will not get picked but small ones should be doable.
unfortunately I cant Code Python or Java Is there any other way to do this? I think there shouldnt be bad spelling Errors, just for small Things like Tailand instead of Thailand or German instead of Germany and so on.
I don’t understand what I’m doing wrong with the last node in my workflow.
As you can see here i get a error message. I dont know what I am doing wrong
what i wanted to do here is to check a list of misspelled country names against a list containing the correct names. e.g. “Algeria” appears in the wrong list but the correct spelling would be “Algeria”. My fuzzy model should now search in the list with the correctly spelled names for the model that is closest to it and replace it with it.
Therefore I first cleaned and unified the data as far as possible and then used the example model for fuzzy matching. I don’t understand what I could have done wrong here.
would be great if someone could help me. I would appreciate it very much
Thanks and Kind regards,
here is my way of solving that Problem.
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