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https://0-doi-org.libus.csd.mu.edu/10.13003/ief7aibi In our previous blog post in this series, we explained why no metadata matching strategy can return perfect results. Thankfully, however, this does not mean that it’s impossible to know anything about the quality of matching. Indeed, we can (and should!) measure how close (or far) we are from achieving perfection with our matching. Read on to learn how this can be done! How about we start with a quiz?

https://0-doi-org.libus.csd.mu.edu/10.13003/pied3tho In our previous instalments of the blog series about matching (see part 1 and part 2), we explained what metadata matching is, why it is important and described its basic terminology. In this entry, we will discuss a few common beliefs about metadata matching that are often encountered when interacting with users, developers, integrators, and other stakeholders. Spoiler alert: we are calling them myths because these beliefs are not true!

https://0-doi-org.libus.csd.mu.edu/10.13003/zie7reeg In our previous blog post about metadata matching, we discussed what it is and why we need it (tl;dr: to discover more relationships within the scholarly record). Here, we will describe some basic matching-related terminology and the components of a matching process. We will also pose some typical product questions to consider when developing or integrating matching solutions. Basic terminology Metadata matching is a high-level concept, with many different problems falling into this category.

https://0-doi-org.libus.csd.mu.edu/10.13003/aewi1cai At Crossref and ROR, we develop and run processes that match metadata at scale, creating relationships between millions of entities in the scholarly record. Over the last few years, we’ve spent a lot of time diving into details about metadata matching strategies, evaluation, and integration. It is quite possibly our favourite thing to talk and write about! But sometimes it is good to step back and look at the problem from a wider perspective.