Short presentation I gave at the Reading Semantic Web meetup about the Linked Data patterns book. The talk outlined the major areas in which we can look for patterns and noted some areas for further work.

1. Linked Data Patterns
Linked Data Meetup, 25/02/2013
@ldodds
http://www.flickr.com/photos/geishaboy500/2382529806/

2. What is a Design Pattern?
...a general reusable solution to a commonly occurring
problem within a given context...
... a description or template for how to solve a problem that
can be used in many different situations...
http://en.wikipedia.org/wiki/Design_pattern_(computer_science)

3. Benefits of Design Patterns
(General)
● Simple focused discussion of a single issue
● Encourage discussion of related or complementary
approaches
● Capture successful, oft-repeated designs (and failures)
● Build a lexicon: a language for practitioners that improves
communication

4. Benefits of Linked Data Patterns
● Supplement existing publishing guides & tutorials
● Provide clear guidance on common problems & questions
● Help people avoid common pit-falls
● Improve quality of published data
● Lower barrier to entry

5. http://patterns.dataincubator.org/book/

6. Identifier Patterns
● How do we partition a URI space?
● How do we use existing keys to build URIs?
● How can we make linking easier?
● How can we work around lack of stable identifiers for
common resources?

7. Modelling Patterns
● How do we get the most from a graph based model?
● How can we best handle semi-structured and/or multi-
lingual data?
● How can we describe complex relationships between
resources?
● How can we annotate relationships between resources?

8. Publishing Patterns
● How do we best publish data on the web?
● How do we link to other sources of data?
● How can we annotate existing data?
● How do we link documents and data?
● How do we enhance data over time?
● How do we remove data from the web?

9. Data Management Patterns
● How can we organise data in a triple store?
● What are the different ways in which we can partition a
graph to make it easier to manage?
● How can we annotate and describe collections of RDF
statements?
● How can we track the sources of RDF statements?

10. Application Patterns
● How do we build applications over RDF data?
● How do we collect and aggregate data from across the
web (or an enterprise?
● How do we validate RDF data?
● How do we transform and merge data?

11. Future Work
● Mixed architectures
● Data conversion work flows
● API design
● Data discovery

12. Book:
http://patterns.dataincubator.org/book/
Source:
http://github.com/ldodds/ld-patterns
Me:
@ldodds