• Classification and Linked Data

    These two modules were developed as part of an IHR Digital project, called the TOBIAS Project, funded by the Arts and Humanities Reseach Council (AHRC) to make the RHS Vocabulary for British and Irish History freely available in various formats. It also tested the feasibility of automatically classifying history materials using the vocabulary and machine learning. Read more about the project.

    The Tutorials: These two tutorials are independent. However, if you are interested in subject classification (but are not very familiar with them) you might find it useful to complete the Classification tutorial before the Linked Data tutorial. 

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    • Instructions: Clicking on the section name will show / hide the section.

    • classificationThis tutorial is a brief and concise introduction to classification and cataloguing. It should give you a clear understanding of these concepts and how they are applied. It has three parts:

      1 Explaining classification and cataloguing 

      2 An introduction to the Bibliography of British and Irish History and classifying using a thesaurus 

      3 Producing a thesaurus - cataloguing, controlled vocabularies, synonyms, homonyms and hierarchies

      View only 'Topic 1'
    • linked data imageThis handbook is a brief and concise introduction to linked open data. It should give you a clear understanding of linked open data, how it is used and how it is created. It has six parts:

      1 Linked data: what is it?

      2 Why is it useful?

      3 Why haven't I heard of it, then?

      4 The URI

      5 RDF and data formats

      6 Querying linked data with SPARQL

      Don't worry: those acronyms will be explained as they arise.

      We expect the handbook to take two or three hours to complete. If you've heard of linked open data before you might complete it more quickly. If this is all new you might find that it's rather abstract the first time round. Really the best way to learn about linked data is to mess around with some!

      What we're not covering is the semantic web proper, which involves things like semantic reasoning over datasets. An example of semantic reasoning would be something like "Danny Millum is the son of Trevor Millum and Valerie Fox is the sister of Trevor Millum". A semantic reasoner should then be able to deduce a new fact: Danny Millum is the nephew of Valerie Fox.

      We hope you enjoy the handbook.

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    • The quizzes offer you the opportunity to test your knowledge. These will open in a new window. They can be accessed below but it would make most sense to complete these quizzes at the end of each relevant page of the course handbooks.

      View only 'Topic 3'