Summary and Schedule
Welcome to Linked Open Data for the
Humanities!
This lesson introduces the core ideas behind Linked
Data, Linked Open Data (LOD), and the
RDF data model. It is designed for learners in the
humanities, but no prior technical or theoretical knowledge is required.
The concepts you will encounter are used across libraries, archives,
museums, digital humanities projects, and many other research
domains.
Throughout the lesson, you will work with short discussions and hands-on activities to understand how structured, interconnected data can support research.
During this course, you will:
- explore how humanities information can be represented as relationships between entities,
- learn how RDF expresses these relationships using
subject–predicate–object relationships,
- understand how URIs identify real-world things unambiguously,
- transform tabular data to RDF
- and see how Linked Data enables collections and datasets to connect across the web.
By the end, you will be able to create small RDF descriptions yourself and evaluate when Linked Open Data is useful in your own research context.
What Is Linked Open Data?
Linked Data is a way of publishing structured information so that connections between entities become explicit and machine-readable. Instead of using tables, Linked Data represents knowledge as a graph, where each fact is expressed as a triple.
Linked Open Data applies the same principles but ensures that the data is openly accessible and reusable. This openness enables institutions and researchers to link information across projects and domains.
For example:
- Vincent van Gogh → was born in → Zundert
- Starry Night → was created by → Vincent van Gogh
When such information is identified with URIs, different datasets can refer to the same person, place, or artwork, making integration possible at web scale.
Why Linked Data Matters in the Humanities
Humanities research often involves:
- complex relationships (people, places, works, events),
- multiple names, languages, and historical contexts,
- information dispersed across collections and institutions.
Graph-based representations such as RDF can capture this richness without forcing it into rigid table structures. Linked Open Data makes these graphs shareable so that knowledge from separate sources can be combined.
| Setup Instructions | Download files required for the lesson | |
| Duration: 00h 00m | 1. Introduction to Linked Open Data in the Humanities |
What is Linked Open Data, and how does it differ from other data
models? Why are standardized identifiers (e.g., URIs) essential for LOD? How can the subject-predicate-object model be used to describe LOD? What are real-world examples of Linked Open Data in the humanities? |
| Duration: 00h 12m | 2. The concept of IRIs |
How do IRIs eliminate ambiguity when different datasets use similar
titles? What are the essential components of a IRI, and how do they work together to ensure uniqueness? Why are namespaces crucial for maintaining clarity and consistency in linked data? |
| Duration: 00h 24m | 3. Introduction to RDF and Basic Modeling |
What is RDF, and why is it used in Linked Open Data? How does RDF structure information? How can we represent real-world relationships using RDF? |
| Duration: 00h 46m | 4. Model Linked Data |
What are IRIs, literals and a blanknodes and when to use them? How to write down RDF? What is Turtle? What is a vocabulary and a namespace? What is RDF Schema and what is it used for? |
| Duration: 00h 58m | 5. Create Linked Data |
What possibilities do I have to write RDF statements? What is OpenRefine useful for with the focus on Linked Open Data? How to create a RDF dataset by using OpenRefine? How do I reconcile my data by comparing it to authoritative datasets? How to export and save the transfomed RDF data with OpenRefine? |
| Duration: 01h 40m | 6. Query Linked Data with SPARQL | What is SPARQL? |
| Duration: 01h 52m | 7. Publish your Linked Data | How do I publish my linked data? |
| Duration: 02h 04m | Finish |
The actual schedule may vary slightly depending on the topics and exercises chosen by the instructor.
FIXME: Setup instructions live in this document. Please specify the tools and the data sets the Learner needs to have installed.
Data Sets
The Dataset we will use in this lesson is a subset from the Collection of the Metropolitan Museum of Art. If you are interested in the whole data you can find and use it in their database here. The subset we are using can be downloaded here
Software Setup
Details
The only software you need for doing this workshop is Open Refine with the extension rdf-transform. OpenRefine is actually a tool for data cleaning. However, with various extensions, the tool can be customised and extended to convert data into other formats just as easily. If you are interested in the other functionalities of Open Refine, we are developing a lesson to this aswell, where you learn more of the fundamental functionalities of OpenRefine.
- Click this link to download OpenRefine.
- Unzip the folder to a place of your choice (such as D:/Program Files/OpenRefine).
- In the unzipped folder doubeclick “openrefine.exe” to install and start OpenRefine.
- Download the extension rdf-transform.
- In the next step you need to unzip this folder into your OpenRefine Workspace. Depending on your Windows version the path for this can be different. You can find your path by: launchING OpenRefine and click Open Project in the sidebar At the bottom of the screen, click Browse workspace directory A file-explorer or finder window will open in your workspace. Note that path
- In the workspace folder, if not already there, create a new folder called “extensions”. Unzip rdf-transform into that folder.
- Close OpenRefine and reopen it.
- Check that you have Firefox, Edge, Opera or Chrome browsers installed and set as your default browser. OpenRefine runs in your default browser. It will not run correctly in Internet Explorer.
- Download the software from openrefine.org.
- Unzip the downloaded file into a directory by double-clicking it. Name that directory something like OpenRefine. / Double click the dmg file. A window opens an now drag and drop the OpenRefine.app into your Applications
- Go to your newly created OpenRefine directory.
- Drag the OpenRefine app into the Applications folder.
- Launch OpenRefine: Control-click the app icon, then choose “Open” from the shortcut menu. For Troubleshooting help, see the Apple support page.
- If you are using a different browser than listed above, or if OpenRefine does not automatically open for you, point your browser at http://127.0.0.1:3333/ or http://localhost:3333 to launch the program.
- Download the extension rdf-transform.
- Unzip the folder and move it into your OpenRefine workspace. * You
can find the path to your OpenRefine Workspace by launching OpenRefine
and click Open Project in the sidebar. Then at the bottom of the screen,
click “Browse workspace directory”. A Finder window with the path
~/Library/Application Support/OpenRefineopens. * Unzip the downloaded folder by double clicking on it. * Move the folderrdf-transforminto the folderextensionin your OpenRefine workspace. - Close OpenRefine and reopen it.
- Click this link to download OpenRefine.
- Unzip the folder to a place of your choice. We recommend using your personal space.
- Open your terminal and navigate into the new OpenRefine folder.
- Write “./refine” into your terminal to install OpenRefine. This should start the program in your browser. Close the browser and close OpenRefine in your terminaml with “Strg+C”.
- Download the extension rdf-transform.
- Unzip the folder into your OpenRefine workspace. The workspace is not the folder you downloaded and unzipped in step 2. By default your workspace is located here: “~/.local/share/openrefine/”. In this workspace, if not already there, create a folder “extensions”. Unzip the folder into this new folder.
- Open your terminal again, navigate again into your OpenRefine folder and start it with “./refine”.