The first method utilized Open Refine and it’s reconciliation services via the API of the focus vocabulary. This method utilized Python script that matched terms in the DPLA dataset with terms from LCSH, LCNAF, and AAT. This method is very time-consuming. Using only a small sample of the dataset consisting of about 796508 terms took about 5-6 hours and returned only about 16% matching terms. (These were exact matches). While this method can definitely be used to find exact matches. Testing should be done to ascertain if the slow speed has to do with the machine and connection specs of the testing machine. However, this method did not prove useful for fuzzy matches. Variant and compound terms were completely ignored unless they matched exactly. Below is an example of the results returned through the reconciliation process.
The scripts used for reconciliation are open source and freely available via GitHub and may be used and modified to suit the needs of the task at hand.
The second method involved obtaining the data locally then constructing a workflow inside the Alteryx Data Analytics platform. To obtain the data, Apache Jena was used to convert the N-Triple files from the Library of Congress and the Getty into comma-separated values format for easy manipulation. These files could then be pulled into the workflow.
The first thing that was done was some data preparation and cleaning. Removing leading and trailing spaces, converting all the labels to lowercase and removing extraneous characters. We then added unique identifiers and source labels to the data to be used later in the process. The data was then joined on the label field to obtain exact matches. This process returned more exact match results than the previous method with the same data, and even with the full (not sample) dataset, the entire process took a little under 5 minutes. The data that did not match was then processed through a fuzzy match tool where various algorithms such as key match, Levenshtein, Jaro, or various combinations of these may be used to process the data and find non-exact matches.
Each algorithm returns differing results and more study needs to be given to which method may be best or which combination yields the best and most consistent results.
What is true of all of the algorithms though is that a match score lower than 85% seems to results in matches that are not quite correct, with correct matches interspersed. Although even high match scores using the character Levenshtein algorithm displays this problem with LCSH compound terms in particular. For example, [finance–law and legislation] is being shown as a match with [finance–law and legislation–peru]. While these are similar, should they be considered any kind of match for the purposes of this exercise? If so, how should the match be described?
Still despite the problems, trying various algorithms and varying the match thresholds returns many more matches than the exact match method only. This method also seems useful for matching terms that were using the LCSH compound term style with close matches in AAT. Below are some examples of results
Character: Best of Levenshtein & Jaro
Word: Best of Levenshtein & Jaro
In the second image, we can look at the example with kerosene lamps. In the DPLA data, it seems to have been labeled using the LCSH format as [lamp–kerosene], but the algorithm is showing it is a close match with the term [lamp, kerosene] in AAT.
The results from these algorithms need to be studied and refined more so that the best results can be obtained consistently. I hope to be able to look more in-depth at these results for a paper or conference at some point and come up with a recommended usable workflow.
This is where I was at the end of the ten weeks and I am hoping to find time to look deeper at this problem. I welcome any comments or thoughts and again want to say how grateful I am for the opportunity to work on this project.