Our new book gets metadata practitioners started with Linked Data.
The Linked Data hype is surrounded by questions, and most of those questions are only answered from the technology perspective. Such answers often insufficiently address the needs of people who just want to publish their data. Practitioners from libraries, archives and museums all over the world have very valuable data that they would love to share, but they often don’t find the right practical guidance to do this. Our new handbook Linked Data for Libraries, Archives and Museums changes that. We wrote it for non-technical people, by combining clear explanations with hands-on case studies.
Several years of touring with the Free Your Metadata initiative has taught me one very important lesson: the things technology people take for granted are not as evident for others. While we have known for ages that automated tools exist to help with cleaning your data, the people who actually work with that data have far less exposure to such rapidly changing technologies. What is considered trivial for information technology professionals can strongly impact the work of practitioners—if only it is explained to them in a clear way.
This handbook is the result of a longterm collaboration with Seth van Hooland. As a researcher in the humanities, he knows the domain, demands and use cases of the libraries/archives/museums world quite well. The complementarity of our skills is reflected in this handbook: you don’t need a technical background to understand what Linked Data is and how you can benefit from it. At the same time, the book covers advanced technologies to clean, link, and publish your metadata.
The road to Linked Data
The book covers five main topics: modelling, cleaning, reconciling, enriching and publishing. All topics are illustrated with hands-on case studies.
In Modelling, you’ll learn the differences between the different ways of representing data. Starting from the very beginnings with CSV, we will discuss XML and relational databases, and how the RDF model is different from those. In the case study, you’ll experience first-hand how to access Linked Data, and how to write SPARQL queries. Download this chapter for free to get started right away.
In the Cleaning chapter, you get to meet your own data, and more specifically, face the small and large inconsistencies and other quality issues that inevitably arise with any dataset. No metadata collection is perfect, yet it is quite amazing how much you can improve their quality using freely available tools. The case study applies this approach to the Schoenberg Database of Manuscripts.
The goal of Reconciling is to connect your structured data to existing datasets in the Linked Data cloud. Instead of using concepts that are local to your dataset, we show you how to reuse existing thesauri on the Web. In the case study, you reconcile the Powerhouse Museum data with the Library of Congress Subject Headings.
Enriching applies the same principles to unstructured data, showing how you can link textual values (such as the omnipresent Description field) to concepts in the Linked Data cloud. Again, we only use freely available technologies, to make this enrichment possible at low cost—even for large datasets. The case study shows this for the metadata of the British Library.
Finally, Publishing tackles the problem of technological change: how can you guarantee your data remains accessible while technologies constantly evolve? We tackle a common misunderstanding I’ve discussed before on this blog. Don’t create an API to publish your data, but reuse the Web’s existing infrastructure. The use case walks you through an example publishing platform with data from the Cooper-Hewitt National Design Museum.
Get this book (and a free sample chapter)
If you’ve always been curious how your institution can benefit from Linked Data easily, this practical handbook helps you get started. It is your no-nonsense highway to Linked Data.