Linked Data is an evolving set of techniques for publishing and consuming data on the Web. Learn how Linked Data can turn the Web into a distributed database and how you can participate. In this session, Bernadette Hyland takes the mystery out of Linked Data by summarizing seven steps to prepare your data sets as Linked Data and announce it so others will use it.
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Bernadette Hyland SemTech 2011 West - Linked Data Cookbook
1. The Joy of Data
A cookbook for publishing
Linked Data on the Web
Bernadette Hyland, CEO
3 Round Stones, Inc
bhyland@3roundstones.com
2. A pragmatic
approach to
publishing & consuming
Linked Data
3. Agenda
• Setting the scene
• Ingredients ... we use a cooking analogy
• Open standards & best practices
• Data modeling without context
• Social contract as a publisher
• Next steps
5. We’ll review
• Converting data into RDF
• The social contract publishers
make
• The importance of announcing
• Where to turn for guidance
6. Why should we care?
• We pretend our organizations are hierarchical -- they aren’t
• Information is power.
• Combining information from different sources is very
powerful.
• The US data warehouse market in 2010 was $10B
• In 2012 expected to grow to $13.5B
7. World changing phenomenon
Linked Data approach, we can begin to address the
• Using
non-hierarchical nature of our organizations
• We can combine information sources
• The W3C has defined standards that enable interoperability
and allow us to freely move data
8. We are sowing the
seeds for nothing
short of a
revolution
9. What does it take?
• The ingredients list ...
• Thinking differently about your
data
• Modeling for re-use
• Summary of process in 7 steps
10.
11. “The change from atoms to bits is irrevocable
and unstoppable”
Being Digital by Nicolas Negroponte
12. We use URIs to describe both bits & atoms ...
Information resources are things that
computers understand, e.g., Web pages, images,
CSS files, etc.
Non-information resources are atoms, e.g.,
people, places, events, things, concepts, etc.
13. • A different way of thinking about
data
• The Open World Assumption
• Lots of URIs
• To be citizen of the world (not
everyone speaks English)
• To publish useful information &
announce it!
17. Publish machine & human
readable content
• Machine readable format
• Human-readable descriptions of your data set
• Increase visibility with search engines
• Include RDFa or other microformats
• Publish a voID description of your RDF dataset
20. There is a Process
Identify Model Name Describe Convert Publish
Maintain
21. Preparation
1. Leverage what exists
• Request a copy of the logical and physical model of the
database(s)
• Obtain data extracts (i.e., databases and/or spreadsheets)
or create data in a way that can be replicated.
22. Modeling the data
2. Model data without context to allow for
reuse and easier merging of data sets
• Traditional
DBAs organize data for specified
Web services or applications.
• With LD, application logic does not drive the
data schema, concepts, etc.
23. Modeling the data
3. Look for real world objects of interest (e.g., people, places,
things, locations, etc.) and model them.
• Investigate how others are already modeling similar or
related data.
• Look for duplication and normalize the data
• Use common sense to decide whether or not to make link
24. Modeling the data ...
4. Connect data from different sources and authoritative
vocabularies (see list of popular vocabularies below).
• Use URIs as names for your objects
25. Modeling the data ...
• Put aside immediate needs of any application
• Don’t think about how an application will use your data
• Do think about time and how the data will change over
time.
26. Convert, Publish & Maintain
5. Write a script or process to convert the data set
repeatedly
6. Publish to the Web and announce it! (more details shortly)
7. Maintenance strategy (more details in the social contract at
the end)
27. Take the plunge ... Be forgiving
• Simplistic data models can still be useful
• Better to make progress with something rather than do
nothing because we cannot be comprehensive and
complete
28. Take an iterative approach
1. Review of modeling decisions
2. Review vocabularies chosen and developed
3. Modify/update data conversion scripts
4. Do a maintenance walk-through with real use cases
5. Show how to explore data with SPARQL and
visualizations
6. Discuss a persistent identifier strategy (think PURLs)
31. Data stewards should....
• Make data accessible via the Web’s standard
access mechanism, specifically http URIs
• Represent data in a common format,
such as RDF/XML, Notation-3 (N3), Turtle, N-
Triples, RDFa, and RDF/JSON
• Provide self describing data
32. Linked Data Formats
• RDF/XML - RDF for XML pipelines
• Turtle - Human-readable RDF
• XHTML with GRDDL transformation
• XHTML with embedded RDFa
• RDF Schema - Describing structure
33.
34. In a tart, smoothie or
margarita ... berries
can be combined in
different ways
36. Guidelines for merging
• URIs name the resources we are describing
• Two people using the same URI are describing the same
thing
• The same URI in two datasets means the same thing
• Graphs from several different sources can be merged;
• Resources with the same URI are considered identical;
• No limitations on which graphs can be merged.
38. •Inform the LOD
developer community
(linkeddata.org, W3 lists)
•Announce to search
engines (RDFa hints, register
to make accessible)
•Publish human readable
descriptions
•Encourage interlinking
•Publish schema as voID
•Include SPARQL
endpoint
39. ACCEPTABLE ROI FOR IT
4% 17%
13%
16%
6 months
49% 12 months
18 months
24 months
More than 24 months
40. The Social Contract ...
The not so fine print
• LOD is a social contract to provide the public with information
• Follow best practices for modeling
• Carefully consider your URI strategy
• Ensure that your LOD remains available where you say it will be
• Publish voID description
• For a government agency ... a data policy is “a must”
• specify data quality and retention, treatment of data thru
secondary sources, restrictions for use, frequency of updates,
public participation, and applicability of this data policy