Join Adresses with different Writings

[JoshuaBoth]

Hello,
I have a list that is manually created and a machine-generated list. Both lists contain address data in different columns (city, street, house number). Since I want to combine the lists using the joiner, I have to make the names city and street identical. However, because there are typing errors in the manually created list, this is not possible. Can I adjust the addresses according to the machine-generated list, so that “Bergstraße” becomes “Bergstr.”, “Müller Weg” becomes “Müllerweg”, or “Werner von Siemens” becomes “Werner-von-Siemens” etc.?

[mlauber71]

@JoshuaBoth you can take a look here.

address deduplication, string similarity and fingerprinting (a collection) –...

You can join together addresses with similarity

Fuzzy Matching on One Column to then form Group IDs

@qdmt you could try and adapt this example: It does work without a ground truth but just will try to group the Term you create into distinctive groups based on the items you put into the string (called “Description”) … [image] … and then the threshold you set for matches (0.25). [image] In the end if possible each items will either be in a group or will be its own group: [image]

Fuzzy match rows in one column

@robvp I once created this workflow that would group addresses without a ground truth against which to match. Maybe you can adapt that. If you apply this it looks something like this: [image] You can edit the threshold which would constitute a similarity (Similarity Search – KNIME Community Hub) and maybe also configure the method. [image] If you set the threshold to 0.33 (instead of 0.25) the result would be this: [image] What you could do would be to try change the order of the wor…

[rfeigel]

You might get some ideas from these:

Simple Fuzzy Match Example – scottf

Fuzzy Matching of Strings – knime