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Learning Linked Data Project
Call for Comments
The Learning Linked Data Project, a planning activity funded under the IMLS
National Leadership Program from October 2011 through September 2012, has taken
a first step towards developing a software platform to help instructors,
students, and independent learners interpret and create Linked Data. The
platform is envisioned to be of use to anyone offering training and education
in Linked Data principles and practice, whether in academia or professional
settings, in online instruction or in classrooms.
As Linked Data is based on data structures of a linguistic nature, the guiding
metaphor for the project is that of designing a "language lab" -- a software
platform for analyzing and manipulating Linked Data in support of a wide range
of pedagogical approaches and expected learning outcomes.
The project has prepared a draft "Inventory of Learning Topics", with an
analysis of software required for such a platform, and posted it for public
review through 15 June 2012 on a blog at:
The document is divided into five short blog pages:
-- Understanding Linked Data [2]: "prerequisite" topics, specific to Linked Data,
which must be grasped before a learner can meaningfully use software tools.
The list of topics is linked to a three-page glossary [9] with definitions of
terminology used.
-- Searching and Querying Linked Data [3]: just as language learners learn
through dialog with native speakers, learners of Linked Data must learn how
to pose queries and explore datasets. Tools for doing so include data
validators, reasoners, query tools, and Semantic Web search engines.
-- Creating and Manipulating RDF Data [4]: In the Linked Data cloud, descriptions of
things and descriptions of the vocabularies used to describe those things are
all considered "data," so many of the basic tools for editing, mapping, converting,
and extracting data may be adapted for different types of data.
-- Visualization [5]: Linked Data is conceptually diagrammatic in nature, and
graphical tools can help the learner explore the statistical, spatial, or
temporal characteristics of datasets by visualizing webs of data at various
levels of granularity or by plotting the data to maps or timelines.
-- Implementing a Linked Data Application [6]: Simply learning how to interpret and
manipulate Linked Data could stop with the topics outlined above. The extent
to which a language-lab-like platform for learning Linked Data should encompass
tools for building real applications poses questions of scope on which the
project would appreciate input.
The project envisions the platform as a basis for the development of course
modules by people involved in both formal and informal learning environments,
so comments about the usefulness of such a platform for particular scenarios
would be especially welcome.
The comments received will be incorporated into a revised document and final
report to be published in September 2012. This report will be used as the basis
for a subsequent IMLS project proposal, to be submitted in early 2013, for
implementing the platform specified.
The partners of the Learning Linked Data Project are the University of
Washington, Kent State University, the University of North Carolina, JES &
Company, and 3 Round Stones, Inc. The project lead and contact person is Mike
Crandall of the University of Washington.
