Joining datasets with fuzzy matching.


Do you know this? Finally you got two datasets containing data about the same thing - all you need to do is join them up to produce your result. Sometimes this is easier said then done: If unique identifiers are missing and names are commonly spelled different, joining data becomes a nightmare. Reconcile-csv aims to reduce this nightmare.

Reconcile-csv is a reconciliation service for OpenRefine running from a CSV file. It uses fuzzy matching to match entries in one dataset to entries in another dataset, helping to introduce unique IDs into the system - so they can be used to join your data painlessly.

Download reconcile-csv-0.1.2

Reconcile-csv is free and open source software released under the BSD license.



To use reconcile-csv you’ll need:

Take one of the two datasets - preferably the more trusted, cleaner or complete dataset - and introduce a column with unique identifiers (this can simply be counting from 1 up, md5-sums of field combinations that are unique or pre-existing unique ids in the dataset).

Starting the Server

To start the reconciliation server download the jar file above, then start it with:

java -Xmx2g -jar reconcile-csv-0.1.2.jar <CSV-File> <Search Column> <ID Column>

CSV-File is the csv file you will use as a basis of reconciliation. As stated above preferably the cleaner, more complete or trusted one. You will introduce the unique IDs from that file to the other file.

Search Column is the primary column you want to use for matching. E.g. you want to match facilities with names spelled slightly differently - this would be the column to add here.

ID Column is the column containing unique ids for the facilities - if you don’t have one: generate one.

Reconciling in OpenRefine

Start OpenRefine and load the dataset, where you want to introduce the unique-ids from the other dataset. Select the column options for the column containing the primary names for matching. this should be the same information than the search column (e.g. if you specified a column of facility names in the search, pick the column containing facility names).

In the options select “Reconcile” and “Start Reconciling”. Add a standard reconciliation service pointing to http://localhost:8000/reconcile. The service will offer you to add additional columns as parameters - if you e.g. also have city names, you could add them and specify the column name containing city names in the other CSV file.

Start the reconciliation - depending on the size of your datasets this might take a while. Fuzzy-matching is no easy feat, and each row in one dataset has to be compared to each row in another dataset.

When done OpenRefine will show you a facet that allows you to select the score the entries got. Reconcile-csv uses a fuzzy-matching algorithm called dice that returns the likelyhood the two compared entries are the same: if it’s 1 it means it’s exactly the same and is automatically matched up.

Look at your results often matches with very high scores (e.g. >0.85) can be automatically matched to the highest scoring entry using the “action” submeny in the reconciliation menu. For the others, go through manually, if you like the link, click the check-mark.

Reconcile-csv also supports searching for matches, if e.g. a match can’t be found but you happen to know the name of the facility in the other dataset, you can click on “search for more” and enter the search term, hovering over an entry will show you additional information to the term, clicking will assign the match.

Once you found all the matches you can find- create the unique id column. Do this by selecting “edit column -> add column based on this column” from the options of the column where you did your matching. Now give the column a name (the same as in the other spreadsheet helps increadibly) and use the expression

to get the IDs for the matches.

Now you can join the datasets using the unique ids


You can help to make reconcile-csv better in the next version. If you find bugs or problems, please report them on the github issues - check whether someone already reported the issue.

If you know clojure (or want to learn it) feel free to fork the repository and submit pull-requests. Also look out for open issues you could resolve.

If you have any questions or want to discuss similar solutions: the OKFNLabs mailinglist or IRC Channel (#okfn on freenode) is the right