The document discusses publishing data sets as linked data using semantic web technologies. It describes treating a data set as a single resource and providing metadata as linked data to support discovery (Approach 1). It also describes publishing the individual records in a data set as linked data to enable integration, comparison and accessing data as a service (Approach 2). It provides a case study of how the Environment Agency published their bathing water quality data set as linked data using these approaches and vocabularies like the Data Cube vocabulary.

1. Semantic web and linked data
for data set publication
Dave Reynolds, Epimorphics Ltd
@der42

2. Outline
 Background on linked data
 Roles in data set publishing
 Case study: Environment Agency
 Lessons

3. Linked data background

4. Linked data ...
publishing data on the web ...
... to enable integration, linking and reuse
across silos

5. Linked data
Apply the principles to the web to publication of data
The linked data web:
 is a global network of things
 each identified by a URI
 fetching a URI gives a set of statements in RDF
 things connected by typed links
 open, anyone can say anything about anything else
Linked data is “data you can click on”

6. Example schools information
http://education.data.gov.uk/id/school/401874

7. Example schools information
http://education.data.gov.uk/id/school/401874 a School
label phase
district “Secondary”
“Cardiff High School”
“Cardiff”

8. Example schools information
http://education.data.gov.uk/id/school/401874 a school:School
phase
label
district school:PhaseOfEducation_Secondary
“Cardiff High School”
http://statistics.data.gov.uk/id/local-authority-district/00PT label “Cardiff”

9. Example schools information
http://education.data.gov.uk/id/school/401874 rdf:type school:School
rdfs:label school:phase
school:district school:PhaseOfEducation_Secondary
“Cardiff High School”
http://statistics.data.gov.uk/id/local-authority-district/00PT rdfs:label “Cardiff”

10. Example schools information
http://education.data.gov.uk/id/school/401874 rdf:type school:School
rdfs:label school:phase
school:district school:PhaseOfEducation_Secondary
“Cardiff High School”
http://statistics.data.gov.uk/id/local-authority-district/00PT label “Cardiff”
http://data.ordnancesurvey.co.uk/id/7000000000025484
admingeo:ward
spatial:extent
admingeo:parish
GML: 310499.4 184176.6 310476.5 ...

11. Example schools information
http://education.data.gov.uk/id/school/401874 rdf:type school:School
rdfs:label school:phase
school:district school:PhaseOfEducation_Secondary
“Cardiff High School”
http://statistics.data.gov.uk/id/local-authority-district/00PT label “Cardiff”
owl:sameAs
http://data.ordnancesurvey.co.uk/id/7000000000025484
admingeo:ward
spatial:extent
admingeo:parish
GML: 310499.4 184176.6 310476.5 ...

13. Role in data set publication
 well suited to describing things
 schools, companies, animal species, music tracks, tv programmes ...
 what about datasets?
 environmental measurements, experimental results, statistical analyses ...

14. Approach 1 : Data catalogues
 treat the dataset as a single resource, identify with a URI
 provide metadata as linked data
 descriptive
 categorical
 technical and structural
Benefits?
 separate of metadata from resource & repository
 easy aggregation of metadata into catalogues
 schema-less enables use-specific annotations and links
 use of sharable category schemes and reference data
=> support for discovery

15. Approach 2 : Fine grain publication
 publish the data set itself as linked data
 entities, terms, individual records in data identified by URIs
 data set structure and ontologies linked from data
 still include dataset metadata
Benefits?
 all benefits of approach 1 to support discovery
 self-describing
 data slices addressable (trace back, provenance, annotation)
 integration across sets - reuse of terms for dimensions, units, values
 fine grained access
=> integration, comparison, context, data as a service

17. bathing water quality
what we do...
start of season
15th May Press interest
bathing season
what information 20-22 samples in 22weeks
is relevant to the public
about beaches
30th Sept
annual report
what November
we do
December

18. how linkable data helps
Tenby
Tourist Information Centre
Unit 2 , The Gateway Complex
Tenby. Wales , SA70 7LT
Tel: 01834 842 402
Fax: 01834 845 439
Email: tenby.tic@pembrokeshire.gov.uk
Photo by Skellig2008 (flickr)

19. Publishing the Bathing Water Quality data set
Bathing Sampling Zones Of Assessment
Vocabularies
Waters Points Influence s
e.g. http://location.data.gov.uk/def/ef/SampingPoint
URI Set
Bathing Sampling Zone Of
Reference Data Waters Points Influence
e.g. http://location.data.gov.uk/so/ef/SamplingPoint/bwsp.eaew
Assessme http://environment.data.gov.uk/data/bathing-water-quality
Observation
nt
Datasets
void:subset void:subset
In-season
Annual
Weekly
.../compliance Complianc .../in-season
Assessme
e
nt

20. Data cube vocabulary
 collaborative development
sponsored by data.gov.uk
 simple, flexible vocabulary
 mirrors core information models from:
 SDMX (Statistical Data and Metadata eXchange)
 DDI (Data Documentation Initiative)
 extension to SCOVO vocabulary
image: dullhunk @ flickr

21. Data cube model
A set of observations
 indexed by dimensions
 describing measures
 interpreted according to attributes
(e.g. region)
dimension
measure(s) attributes
• population unit of measure = count
= 32,567 status = preliminary
...
dimension
(e.g. time)

22. Data cube vocabulary
1. Top level
 DataSet qb:DataStructureDefinition
qb:component
 provenance and metadata qb:sliceKey
 structure qb:structure
qb:DataSet qb:SliceKey
qb:slice
qb:sliceStructure
qb:dataset
qb:Slice
qb:subSlice
qb:observation
qb:Observation
dimension values
measure value(s)
attribute values

23. Data cube vocabulary
1. Top level
 DataSet qb:DataStructureDefinition
qb:component
 provenance and metadata qb:sliceKey
 structure qb:structure
 Observation qb:DataSet qb:SliceKey
 measured values, at dimensions qb:slice
qb:sliceStructure
qb:dataset
with attributes qb:Slice
 direct link to DataSet qb:subSlice
qb:observation
qb:Observation
dimension values
measure value(s)
attribute values

24. Data cube vocabulary
1. Top level
 DataSet qb:DataStructureDefinition
qb:component
 provenance and metadata qb:sliceKey
 structure qb:structure
 Observation qb:DataSet qb:SliceKey
 measured values, at dimensions qb:slice
qb:sliceStructure
qb:dataset
with attributes qb:Slice
 direct link to DataSet qb:subSlice
 Slice qb:observation
qb:Observation
 optional grouping by fixing
dimensions dimension values
measure value(s)
attribute values
 guide to presentation
 allows for abbreviated data

25. Data cube vocabulary
2. Data Structure Definition
 explicit definition of cube
qb:DataSet
structure, inline in the data qb:structure
 enables qb:DataStructureDefinition
 validation qb:component
 visualization
 discovery qb:ComponentSpecification
 abbreviation qb:componentRequired
qb:componentAttachment
qb:order
qb:dimension
qb:measure
qb:attribute

26. Bathing Water Quality cubes
 measures
 total coliform count, entero virus count, ...
 sample classification
 dimensions
 sampling point
 sampling week
 sampling year
 attributes
 abnormal weather

27. Everything has a URI
 Selected Lists and
Individual Bathing Waters
 Lists and Individual
Assessments
 In-Season or Annual
Compliance
 Vocabulary Terms
 Datasets (and subsets)
 Presented as:
 HTML, (for people)
 JSON, XML, RDF and CSV
(for programs)

28. Data Platform and Applications
Web of Linked Data
http://environment.data.gov.uk/lab/bwq-os.html

29. Outcomes
 bathing water quality information available
 as both data set and set of web APIs
 updated weekly (in season)
 third party applications to use and combine the data
 seed a web of environmental and location data
 reference identifiers can be reused for related information
 URI patterns designed to be compatible with INSPIRE

30. Wrapping up
image: erika g. @ flickr.com

31. Lessons
 importance of reference identifiers
 developer accessibility
 linked data API
 publish once, consume many ways
 importance of maintenance and QoS expectation
 reusable patterns:
 reusable vocabularies - Data Cube, org ...
 URI patterns
 provenance – OMPV and specializations
 incremental approach

32. Acknowledgements
 Alex Coley (Environment Agency)
 for slides 17, 18, and for sponsoring the bathing water quality
data publication
 Stuart Williams
 developer of the bathing water application and slides 19,27,28
 John Sheridan (The National Archive)
 for sponsoring the development of data cube
 Richard Cyganiak, Jeni Tennison
 co-developers of the data cube vocabulary

33. fin.
fin.
image: Christian Haugen @ flickr.com

34. Spare

35. Linked data principles
 Use URIs as names for things
 Use HTTP URIs so that people can look up those names
 When someone looks up a URI, provide useful
information, using the standards (RDF*, SPARQL)
 Include links to other URIs, so that they can discover
more things
Pattern of application of semantic
web stack

36. Linked open data cloud: 2007
Linking Open Data cloud diagram, by Richard Cyganiak and Anja Jentzsch. http://lod-cloud.net/

37. Linked open data cloud: 2009
Linking Open Data cloud diagram, by Richard Cyganiak and Anja Jentzsch. http://lod-cloud.net/

38. Linked open data cloud: 2010
Linking Open Data cloud diagram, by Richard Cyganiak and Anja Jentzsch. http://lod-cloud.net/

39. Accessing all this data
 link following
 HTTP GET, follow links, aggregate relevant statements
 query
 SPARQL

40. SPARQL
 core idea is pattern matching
 graph patterns with variables
 any subgraph which matches yields row of bindings
ont:districtAdministrative rdfs:label
?school [] “Cardiff”
 syntax based on Turtle syntax for RDF
 web API endpoints
 lots of power
 filters  sub-queries  federated query
 optionals  property chains  update
 named graphs  aggregation  construct

41. Accessing all this data
 link following
 HTTP GET, follow links, aggregate relevant statements
 query
 SPARQL
 linked data API
 RESTful API onto linked data resources
 simple query, usable without RDF stack, web dev friendly
 easy to layer visualizations and UIs on top
 third parties
 search engines and aggregators e.g. Sindice, sameAs.org

42. Semantic web layer cake

43. Data.gov.uk
visualizations on top of linked data

44. Data.gov.uk – linked datasets and APIs