Creating and Manipulating RDF Data

In the Resource Description Framework (RDF), “everything is data,” from descriptions of things — both of things in the world or, more specifically, of information resources, to descriptions of the Languages of Description used to describe those things (see RDF Data). Instances, attribute spaces, and value spaces are all expressed in the same RDF language. The category “creating and manipulating data,” therefore, encompasses a broad range of topics which, in other fields, might be considered quite separate from each other:

  • Creating RDF Vocabularies and minting URIs for their properties and classes
    • RDF vocabulary editors
  • Creating property-to-property and class-to-class links across RDF Vocabularies
    • Mapping tools
  • Creating a Domain Model enumerating the things to be described in a dataset
    • Diagramming tools (e.g., UML and mind maps)
  • Creating other types of datasets
    • Data editors
  • Converting triples among alternative RDF syntaxes (e.g., RDF/XML, Turtle, N-Triples, RDFa)
    • Triple converters (e.g., Rapper)
  • Generating RDF Triples from the content analysis of unstructured text data
    • Triplifiers for full text (e.g., Calais)
  • Deriving RDF triples from non-RDF data

9 thoughts on “Creating and Manipulating RDF Data

  1. Much of the legacy data is stored in relational databases or other formats such as spreadsheet or CVS files. Converting such structured data yet not in RDF syntax has already been a big problem even though with tools like D2R server. A significant chunk of this section should be devoted to the techniques and tools for converting non-RDF, structured data into linked data. As Big Data hype is on the rise, we can expect a great demand for the knowledge and skills in this area.

    • What tools would you (and others) use for converting non-RDF, structured data into Linked Data?

  2. I would definitely put a section on standards here. We find an ongoing challenge when aligning with others based on varying use of properties, minting new properties instead of selecting existing properties, etc. Just using RDF is not enough, and a section here that goes into detail on current widely used properties, how they related and why it’s good to use them would be great.

  3. Since almost all the other headings contain “Linked Data”, it seems that it is better to use “Linked Data” instead of “RDF Data”.
    URIs are extremely important for Linked Data. For beginners, designing URIs for their Linked Data set may be the first hurdle to face. The Linked Data Patterns ( may be a good resource to learn existing good design patterns of Linked Data.

  4. The final version of the Inventory of Learning Topics, with modifications in light of comments received, is posted here:

    The project sponsors are grateful for the input received so far, and they invite additional comments on the page linked above. All additional input will inform future implementation plans for the Learning Linked Data project.

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