Use Cases

In addition to comments on the Learning Inventory, the project invites contributions of use cases outlining possible instructional scenarios related to learning Linked Data concepts, technologies, and tools. Such practical applications can help to discover and prioritize tools to be highlighted in implementing a coherent package of instructional resources.

Stuart Sutton, CEO of the Dublin Core Metadata Initiative, has provided a use case for an introductory university course. Others may be posted as comments on this page.

SCENARIO: Introductory university course in semantic metadata

–Prerequiste knowledge and skills:
Learners should have a basic understanding of knowledge organization systems, XML, and database management. No prior knowledge of RDF or Semantic Web is assumed.

–Education or training context:
Learners should be motivated to learn the technology. Appropriate for advanced undergraduate informatics majors. Instruction — lectures and discussion — are totally online and asynchronous. A ten-week course, with roughly 20 hours of lectures and presentation and 70 hours of reading, assignments, and other activities.

–Student deliverables:
By the end of the course, students will produce serializations of RDF graphs in several syntaxes; design a domain model (class diagram); create RDF vocabularies and SKOS concept schemes; and produce RDF instance data for a student project.

–Expected learning outcomes:
Students should demonstrate a grasp of basic Linked Data and Semantic Web tools and concepts, including the principles and mechanisms for merging graphs. They should understand how to use RDF serialization syntaxes; manually draw RDF graphs; serialize frequently used N-ary triple patterns; “webify” existing controlled vocabularies. They should be able to explain the difference between the XML information set and the RDF abstract data model and demonstrate modeling skills in mapping between the two.

–Required use of tools:
Students should be able to use tools for graphically depicting domain models (class diagrams); for editing and validating RDF data; for transforming data among different RDF syntaxes; and for generating visual depictions of RDF graphs

2 thoughts on “Use Cases

  1. Another use case (in very rough draft) is based on experience engaging with object descriptions from museums during a project at the Univ. of Washington iSchool. Many museums have exposed collection metadata as Linked Data, and more of them may well be inspired to follow suit. The learning requirements for such a project are largely conjecture on my part, but maybe they’ll trigger some welcome edits based on wider experience. I’ve followed Stuart’s format.

    — David

    SCENARIO: Museum interested in exposing collection descriptions as semantic metadata

    –Prerequiste knowledge and skills:
    Learners should have a basic understanding of knowledge organization systems, and at least intermediate understanding of the local collection management system, possibly including any web publishing modules available. Some skills/understanding in relational databases needed?

    –Education or training context:
    Motivation to engage with other cultural institutions is probably a given. Individual learner would be a web developer or web producer, but instruction may have to engage with a policy-level decision maker and primary collection manager(s) with responsibility for collection record content. Instruction strategy would emphasize self-instruction, probably based on text accessed over the web, maybe some video. Maybe some trial & error on tool demo consoles. Engagement via discussion boards with other learner/practitioners? Timeline would likely depend on project implementation plans, or it could be quite indefinite as a side project emphasizing background knowledge.

    –Student deliverables:
    Deliverables would likely be much the same as for other use cases, but maybe emphasizing working test/demo prototypes, and ultimately working production instances, more than rigorous document artifacts. Training may need to emphasize the value of documentation, e.g., for project revisions over time.

    –Expected learning outcomes:
    Highly practical! RDF/XML, Turtle, other(?) encodings for sample data from actual collection metadata; working display templates that mash up the museum’s own data with relevant data from other institutions; server and software support specs and configurations, e.g. for a SPARQL endpoint.
    One key outcome might be knowledge of available data sources relevant to the museum’s collection. Trust relationships could be an important variable, depending on collection scope and type as well as particular application goals. A general natural history museum might happily source Linked Data from Wikipedia for a public education application, while a very specialized institution might select carefully for sources with specific expertise.
    Persistent publishing of application profiles — A specialized institution might encounter highly detailed needs for resource description within its domain. If appropriate vocabularies don’t yet exist, it may have to develop them, publish them in a persistent way, and provide for their maintenance over time, including other stakeholders within the domain in those processes.

    –Required use of tools:
    RDF editors and validators; utilities for mediating between back-end systems, likely proprietary vendor systems, and Linked Data output needs, e.g. SPARQL access; SPARQL query builders, consoles; tools for graphic modeling (e.g., class diagrams); use and adaptation of public metadata repositories to publish specialized vocabularies.

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

    http://lld.ischool.uw.edu/wp/learning/inventory/

    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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