In 2020 we released our first public data file, something we’ve turned into an annual affair supporting our commitment to the Principles of Open Scholarly Infrastructure (POSI). We’ve just posted the 2022 file, which can now be downloaded via torrent like in years past.
We aim to publish these in the first quarter of each year, though as you may notice, we’re a little behind our intended schedule. The reason for this delay was that we wanted to make critical new metadata fields available, including resource URLs and titles with markup.
Unfortunately, Bryan Vickery has moved onto pastures new. I would like to thank him for his many contributions at Crossref and we all wish him well.
I’m now pleased to announce that Rachael Lammey will be Crossref’s new Director of Product starting on Monday, May 16th.
Rachael’s skills and experience are perfectly suited for this role. She has been at Crossref since 2012 and has deep knowledge and experience of all things Crossref: our mission; our members; our culture; and our services.
Since we announced last September the launch of a new version of iThenticate, a number of you have upgraded and become familiar with iThenticate v2 and its new and improved features which include:
A faster, more user-friendly and responsive interface A preprint exclusion filter, giving users the ability to identify content on preprint servers more easily A new “red flag” feature that signals the detection of hidden text such as text/quotation marks in white font, or suspicious character replacement A private repository available for browser users, allowing them to compare against their previous submissions to identify duplicate submissions within your organisation A content portal, helping users check how much of their own published content has been successfully indexed, self-diagnose and fix the content that has failed to be indexed in iThenticate.
A re-cap We kicked off our Ambassador Program in 2018 after consultation with our members, who told us they wanted greater support and representation in their local regions, time zones, and languages.
We also recognized that our membership has grown and changed dramatically over recent years and that it is likely to continue to do so. We now have over 16,000 members across 140 countries. As we work to understand what’s to come and ensure that we are meeting the needs of such an expansive community, having trusted local contacts we can work closely with is key to ensuring we are more proactive in engaging with new audiences and supporting existing members.
In my previous blog post, Matchmaker, matchmaker, make me a match, I compared four approaches for reference matching. The comparison was done using a dataset composed of automatically-generated reference strings. Now it’s time for the matching algorithms to face the real enemy: the unstructured reference strings deposited with Crossref by some members. Are the matching algorithms ready for this challenge? Which algorithm will prove worthy of becoming the guardian of the mighty citation network? Buckle up and enjoy our second matching battle!
I evaluated and compared four reference matching approaches: the legacy approach based on reference parsing, and three variants of search-based matching.
The dataset comprises 2,000 unstructured reference strings from the Crossref metadata.
The metrics are precision and recall calculated over the citation links. I also use F1 as a standard single-number metric that combines precision and recall, weighing them equally.
The best variant of search-based matching outperforms the legacy approach in F1 (96.3% vs. 92.5%), with the precision worse by only 0.9% (98.09% vs. 98.95%), and the recall better by 8.9% (94.56% vs. 86.85%).
Common causes of SBMV’s errors are: incomplete/erroneous metadata of the target documents, and noise in the reference strings.
The results reported here generalize to the subset of references in Crossref that are deposited without the target DOI and are present in the form of unstructured strings.
In reference matching, we try to find the DOI of the document referenced by a given input reference. The input reference can have a structured form (a collection of metadata fields) and/or an unstructured form (a string formatted in a certain citation style).
In my previous blog post, I used reference strings generated automatically to compare four matching algorithms: Crossref’s legacy approach based on reference parsing and three variations of search-based matching. The best algorithm turned out to be Search-Based Matching with Validation (SBMV). SBMV uses our REST API’s bibliographic search function to select the candidate target documents, and a separate validation-scoring procedure to choose the final target document. The legacy approach and SBMV achieved very similar average precision, and SBMV was much better in average recall.
This comparison had important limitations, which affect the interpretation of these results.
First of all, the reference strings in the dataset might be too perfect. Since they were generated automatically from the Crossref metadata records, any piece of information present in the string, such as the title or the name of the author, will exactly match the information in Crossref’s metadata. In such a case, a matcher comparing the string against the record can simply apply exact matching and everything should be fine.
In real life, however, we should expect all sorts of errors and noise in the reference strings. For example, a string might have been manually typed by a human, so it can have typos. The string might have been scraped from the PDF file, in which case it could have unusual unicode characters, ligatures or missing and extra spaces. A string can also have typical OCR errors, if it was extracted from a scan.
These problems are typical for messy real-life data, and our matching algorithms should be robust enough to handle them. However, when we evaluate and compare approaches using the perfect reference strings, the results won’t tell us how well the algorithms handle harder, noisy cases. After all, even if you repeatedly win chess games against your father, it doesn’t mean you will likely defeat Garry Kasparov (unless, of course, you are Garry Kasparov’s child, in which case, please pass on our regards to your dad!).
Even though I attempted to make the data more similar to the noisy real-life data by simulating some of the possible errors (typos, missing/extra spaces) in two styles, this might not be enough. We simply don’t know the typical distribution of the errors, or even what all the possible errors are, so our data was probably still far from the real, noisy reference strings.
The differences in the distributions are a second major issue with the previous experiment. To build the dataset, I used a random sample from Crossref metadata, so the distribution of the cited item types (journal paper, conference proceeding, book chapter, etc.) reflects the overall distribution in our collection. However, the distribution in real life might be different if, for example, journal papers are on average cited more often than conference proceedings.
Similarly, the distribution of the citation styles is most likely different. To generate the reference strings, I used 11 styles distributed uniformly, while the real distribution most likely contains more styles and is skewed.
All these issues can be summarized as: the data used in my previous experiment is different from the data our matching algorithms have to deal with in the production system. Why is this important? Because in such a case, the evaluation results do not reflect the real performance in our system, just like the child’s score on the math exam says nothing about their score on the history test. We can hope my previous results accurately showed the strengths and weaknesses of each algorithm, but the estimations could be far off.
So, can we do better? Sure!
This time, instead of automatically-generated reference strings, I will use real reference strings found in the Crossref metadata. This will give us a much better picture of the matching algorithms and their real-life performance.
This time the evaluation dataset is composed of 2,000 unstructured reference strings from the Crossref metadata, along with the target true DOIs. The dataset was prepared mostly manually:
First, I drew a random sample of 100,000 metadata records from the system.
Second, I iterated over all sampled items, and extracted those unstructured reference strings, that do not have the DOI provided by the member.
Next, I randomly sampled 2,000 reference strings.
Finally, I assigned a target DOI (or null) to each reference string. This was done by verifying DOIs returned by the algorithms and/or manual searching.
The metrics this time are based on the citation links. A citation link points from the reference (or the document containing the reference) to the referenced (target) document.
When we apply a matching algorithm to a set of reference strings in our collection, we get a set of citation links between our documents. I will call those citation links returned links.
On the other hand, in our collection we have real, true links between the documents. In the best-case scenario, the set of true links and the set of returned links are identical. But we don’t live in a perfect world and our matching algorithms make mistakes.
To measure how close the returned links are to the true links, I used precision, recall and F1. This time they are calculated over all citation links in the dataset. More specifically:
Precision is the fraction of the returned links that are correct. Precision answers the question: if I see a citation link A->B in the output of a matcher, how certain can I be that paper A actually cites paper B?
Recall is the percentage of true links that were returned by the algorithm. Recall answers the question: if paper A cites paper B and B is in the collection, how certain can I be that the matcher’s output contains the citation link A->B?
F1 is the harmonic mean of precision and recall.
In the previous experiment, I also used precision, recall and F1, but they were calculated for each target document and then averaged. This time precision, recall and F1 are not averaged but simply calculated over all citation links. This is a more natural approach, since now the dataset comprises isolated reference strings rather than target documents, and in practice each target document has at most one incoming reference.
I tested the same four approaches as before:
the legacy approach, based on reference parsing
SBM with a simple threshold, which searches for the reference string in the search engine and returns the first hit, if its relevance score exceeds the predefined threshold
SBM with a normalized threshold, which searches for the reference string in the search engine and returns the first hit, if its relevance score divided by the string length exceeds the predefined threshold
SBMV, which first applies SBM with a normalized threshold to select a number of candidate items, and a separate validation procedure is used to select the final target item
All the thresholds are parameters which have to be set prior to the matching. The thresholds used in the experiments were chosen using a separate dataset, as the values maximizing the F1 of each algorithm.
The plot shows the overall results of all tested approaches:
The exact values are also given in the table (the best result for each metric is bolded):
SBM (simple threshold)
SBM (normalized threshold)
As we can see, the legacy approach is the best in precision, slightly outperforming SBMV. In recall, SBMV is clearly the best, which also decided about its victory over the legacy approach in F1.
How do these results compare to the results from my previous blog post? The overall trends (the legacy approach slightly outperforms SBMV in precision, and SBMV outperforms the legacy approach in recall and F1) are the same. The most important differences are: 1) on the real dataset SBM without validation is worse than the legacy approach, and 2) this time the algorithms achieved much higher recall. These differences are most likely related to the difference in data distributions explained before.
SBMV’s strengths and weaknesses
Let’s look at a few example cases where SBMV successfully returned the correct DOI, while the legacy approach failed.
Lundqvist D, Flykt A, Ohman A: The Karolinska Directed Emotional Faces - KDEF, CD ROM from Department of Clinical Neuroscience, Psychology section, Karolinska Institutet. 1998
This is an example of a book chapter. The reference string contains special quotes and dash characters.
R. Schneider,On the Aleksandrov-Fenchel inequality, inDiscrete Geometry and Convexity (J. E. Goodman, E. Lutwak, J. Malkevitch and R. Pollack, eds.), Annals of the New York Academy of Sciences440 (1985), 132–141.
In this case the space is missing between the title and the journal name.
Ono , N. 2011 Stable and fast update rules for independent vector analysis based on auxiliary function technique Proceedings of IEEE Workshop on Applications of Signal Processing to Audio and Acoustics 189 192
In this case both title and journal name are missing from the reference string.
We can see from these examples that SBMV is fairly robust and able to deal with a small amount of noise in the metadata and reference strings.
What about the errors SBMV made? From the perspective of citation links, we have two types of errors:
False positives: incorrect links returned by the algorithm.
False negatives: links that should have been returned but weren’t.
When we apply SBMV instead of the legacy approach, the fraction of false positives within the returned links increases from 1.05% to 1.91%, and the fraction of false negatives within the true links decreases from 13.15% to 5.44%. This means with SBMV:
1.91% of the links in the algorithm’s output are incorrect
5.44% of the true links are not returned by the algorithm
We can also classify all the references in the dataset into several categories, based on the values of true and returned DOIs:
We have the following categories:
References matched to correct DOIs (1129 cases, returned and true blue)
References correctly not matched to anything (791 cases, returned and true white)
References not matched to anything, when they should be (58 cases, returned white, true grey)
References matched to wrong DOIs (7 cases, returned red, true yellow)
References matched to something, when they shouldn’t be matched to anything (15 cases, returned black, true white)
Note that in terms of these categories, precision is equal to:
And recall is equal to:
What are the most common causes of SBMV’s errors?
Incomplete or incorrect Crossref metadata. Even a perfect reference string formatted in the most popular citation style will not be matched, if the target record in the Crossref collection has many missing or incorrect fields.
Similarly, missing or incorrect information in the reference string is very problematic for the matchers.
Errors/noise in the reference string, such as:
HTML/XML markup not stripped from the string
multiple references mixed in one string
spacing issues and typos
In a few cases a document related to the real target was matched, such as the book instead of its chapter, or the conference proceedings paper instead of the thesis.
The most important limitation is the size of the dataset. Every item had to be verified manually, which significantly limited the possibility of creating a large set and also using a lot of independent sets.
Finally, the numbers reported here still don’t reflect the overall precision and recall of the current links in the Crossref metadata. This is because:
we still use the legacy approach for matching,
some references are deposited along with the target DOIs and are not matched by Crossref, these links are not analyzed here, and
in Crossref we have both unstructured and structured references, and in this experiment only the unstructured ones were tested.
The next experiment will be related to the structured references. Similarly as here, I will try to estimate the performance of the search-based matching approach and compare it to the performance of the legacy approach.