Hi, I have a list of Real Names of countries as dictionary values and another sheet have Names of countries which is having spelling mistakes/jumbled string values. The solution i am looking is to have a model which is identifying the closest Name of the countries from the list of Real values (dictionary) while looking to the jumbled / spelling mistake items of country names and to create a new column which is suggesting the closest country name. This will help me to identify and replace the incorrect spelling values with the Real values. Please suggest. Thanks in advance.

@gokulpnair maybe you can adapt this example [image] Getting Started With ML KNIME Analytics Platform Another approach here might be to use the Similarity Search node with Levenshtein distance - you feed the misspellings to the top port and your dictionary to the middle port, and let the node find the best match. I just made a simple workflow (based on an exercise from our L1-DW course) that uses your data:

Thanks @mlauber71 I tried similarly search and it worked good above 85% similarity level. Since there are unmapped items, any other training model can be useful in this scenario? Thanks

@gokulpnair could you just assign the rest manually since there is a finite number of countries. Maybe also store the results and build a workflow that would report unknown versions of writing. Also you could experiment with several settings of the similarity search. You could also try to employ a LLM and a vector store. Here you would have to experiment with prompts and how to best extract the values. [image] Llama3 and KNIME — Build your local Vector Store from PDFs and other Documents … it also runs on your KNIME 4 installation — and on Python Reading time: 6 min read

How to create a model to identify the Closest string values in Knime while comparing to a set of Real values?

KNIME Analytics Platform

mlauber71 July 1, 2024, 5:57pm 2

@gokulpnair maybe you can adapt this example